Install necessary packages

install.packages("summarytools")
WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:

https://cran.rstudio.com/bin/windows/Rtools/
trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.6/summarytools_0.9.8.zip'
Content type 'application/zip' length 914980 bytes (893 KB)
downloaded 893 KB
package ‘summarytools’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\yoges\AppData\Local\Temp\RtmpMv2rvd\downloaded_packages
library(DT)
library(readr)
library(ggplot2)
library(knitr)
library(hms)
library(kableExtra)
library(dplyr)
library(magrittr)
library(gridExtra)

library(plotly)
library(Hmisc)
library(ggthemes)
library(tidyr)
library(sysfonts)
font_add_google(name = "Amatic SC", family = "amatic-sc")

library(sf)
library(rnaturalearth)
library(rnaturalearthdata)
library(summarytools)

Import datasets

vaccinations = read_csv("country_vaccinations.csv")
world_population = read_csv("world_population_by_countries.csv")

Dataset Preview

datatable(vaccinations, options = list(
  searching = FALSE,
  pageLength = 10,
  lengthMenu = c(10, 10, 15, 20)
))
datatable(world_population, options = list(
  searching = FALSE,
  pageLength = 10,
  lengthMenu = c(10, 10, 15, 20)
))

Descriptive Stats

describe(vaccinations, descript = "Life Expectancy") %>% html()
Life Expectancy

15 Variables 4908 Observations
country
nmissingdistinct
49080122
lowest愼㸰:Albania Algeria Andorra Anguilla Argentina
highest:United StatesUruguay Venezuela Wales Zimbabwe
iso_code
nmissingdistinct
4592316118
lowest愼㸰: AIA ALB AND ARE ARG , highest: URY USA VEN ZAF ZWE
date
image
          n    missing   distinct       Info       Mean        Gmd        .05 
       4908          0         85          1 2021-01-31      21.73 2020-12-29 
        .10        .25        .50        .75        .90        .95 
 2021-01-04 2021-01-17 2021-02-03 2021-02-16 2021-02-24 2021-02-27
lowest愼㸰:2020-12-082020-12-092020-12-102020-12-112020-12-12
highest:2021-02-262021-02-272021-02-282021-03-012021-03-02
total_vaccinations
image
        n  missing distinct     Info     Mean      Gmd      .05      .10      .25 
     3193     1715     3053        1  1794351  3023396     1511     6830    32133 
      .50      .75      .90      .95 
   212940   914303  3847174  7627234
lowest愼㸰: 0 1 5 12 13 , highest: 70454064 72806180 75236003 76899987 78631601
people_vaccinated
image
        n  missing distinct     Info     Mean      Gmd      .05      .10      .25 
     2718     2190     2611        1  1546290  2594905     2166     8226    29344 
      .50      .75      .90      .95 
   196111   839216  3224232  6856934
lowest愼㸰: 0 1 5 13 18 , highest: 47184199 48435536 49772180 50732997 51755447
people_fully_vaccinated
image
        n  missing distinct     Info     Mean      Gmd      .05      .10      .25 
     1845     3063     1754        1   529155   893062     1328     3328    12600 
      .50      .75      .90      .95 
    57835   299518  1094752  1843910
lowest愼㸰: 1 2 5 8 12 , highest: 22613359 23698627 24779920 25466405 26162122
daily_vaccinations_raw
image
        n  missing distinct     Info     Mean      Gmd      .05      .10      .25 
     2694     2214     2500        1    74533   120183    104.7    578.6   2382.5 
      .50      .75      .90      .95 
  11975.0  50589.8 172086.8 331940.7
lowest愼㸰: -50012 0 1 2 3 , highest: 2218752 2231326 2242472 2352116 2429823
daily_vaccinations
image
nmissingdistinctInfoMeanGmd.05.10.25.50.75.90.95
4748160357615711396274 126 275 1128 5902 27571120024288104
lowest愼㸰: 1 2 3 4 9 , highest: 1738321 1795238 1817502 1916190 1942788
total_vaccinations_per_hundred
image
nmissingdistinctInfoMeanGmd.05.10.25.50.75.90.95
31931715130317.36510.3 0.016 0.090 0.600 2.820 7.07019.50429.772
lowest愼㸰: 0.00 0.01 0.02 0.03 0.04 , highest: 99.05 102.70 106.53 109.25 112.96
people_vaccinated_per_hundred
image
nmissingdistinctInfoMeanGmd.05.10.25.50.75.90.95
27182190108915.9718.049 0.02 0.10 0.64 2.60 5.5516.8627.29
lowest愼㸰: 0.00 0.01 0.02 0.03 0.04 , highest: 60.68 64.09 67.41 69.80 72.28
people_fully_vaccinated_per_hundred
image
nmissingdistinctInfoMeanGmd.05.10.25.50.75.90.95
1845306347512.3863.4490.0200.0400.2400.8802.0803.1968.418
lowest愼㸰: 0.00 0.01 0.02 0.03 0.04 , highest: 39.13 39.45 39.81 40.48 40.68
daily_vaccinations_per_million
image
        n  missing distinct     Info     Mean      Gmd      .05      .10      .25 
     4748      160     2503        1     2462     3287    30.35    77.00   325.00 
      .50      .75      .90      .95 
  1056.00  2290.50  6233.90  9450.70
lowest愼㸰: 0 1 2 3 4 , highest: 31700 35027 41344 47947 54264
vaccines
image
nmissingdistinct
4908024
lowest愼㸰:Covaxin, Oxford/AstraZeneca EpiVacCorona, Sputnik V Johnson&Johnson Moderna Moderna, Oxford/AstraZeneca, Pfizer/BioNTech
highest:Pfizer/BioNTech, Sinovac Sinopharm/Beijing Sinopharm/Beijing, Sinopharm/Wuhan, Sinovac Sinovac Sputnik V
source_name
image
nmissingdistinct
4908074
lowest愼㸰:Cayman Islands Government Centers for Disease Control and Prevention COVID-19 Malta Public Health Response Team COVID-19 Vaccine Information Platform Department of Statistics and Health Information
highest:Saudi Health Council Sciensano Secretary of Health Social Security Institute Statens Serum Institut
source_website
nmissingdistinct
49080118
lowest愼㸰:http://covid19.ncema.gov.ae/en http://datos.salud.gob.ar/dataset/vacunas-contra-covid-19-dosis-aplicadas-en-la-republica-argentina http://english.ahram.org.eg/NewsContent/1/64/399964/Egypt/Politics-/,-medical-staff-vaccinated-against-COVID-in-first-.aspx http://mohfw.gov.in/pdf/CumulativeCOVIDVaccinationCoverageReport2March2021.pdf http://ncv.kdca.go.kr/
highest:https://www.reuters.com/article/us-health-coronavirus-venezuela-vaccine/venezuela-inoculates-health-workers-in-caracas-hospital-idUSKBN2AM2FR https://www.rnz.co.nz/news/national/437432/covid-19-vaccinations-at-port-of-tauranga-begin https://www.rts.rs/page/stories/sr/%D0%9A%D0%BE%D1%80%D0%BE%D0%BD%D0%B0%D0%B2%D0%B8%D1%80%D1%83%D1%81/story/3134/koronavirus-u-srbiji/4279039/korona-srbija-podaci-zarazeni#shortStory-122262https://www.sainthelena.gov.sh/2021/news/covid-19-vaccination-programme-update/ https://www.terviseamet.ee/et/uudised/covid-19-blogi-2-marts-oopaevaga-lisandus-1113-positiivset-testi

Countrywise Plots

Plot – Vaccines per day | Percentage of vaccined population | Daily Vaccinations

Argentina - AR

argentina <- vaccinations[grep("ARG", vaccinations$iso_code),]
argentina_world_pop <- world_population[grep("Argentina", world_population$Country),]


argentina$population = argentina_world_pop$Population
argentina$percentage = (argentina$people_vaccinated/argentina$population)*100

plot_argentina <- argentina %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇦🇷") +
    scale_y_continuous(breaks = seq(0, 100000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_argentina_1 = plot_argentina
plot_argentina_1 = plot_argentina_1 + ggtitle("Population Vaccinated on \n Argentina (2021)") + 
    theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))

plot_argentina_percentage <- argentina %>%
  ggplot( aes(x=date, y=percentage, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇦🇷") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_argentina_2 = plot_argentina_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_argentina_2 = plot_argentina_2 + ggtitle("Population (%) Vaccinated \n on Argentina") + 
    theme(plot.title = element_text(size = 7, face = "bold"))


plot_argentina_daily_vac <- argentina %>%
  ggplot( aes(x=date, y=daily_vaccinations)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇦🇷") +
    scale_y_continuous(breaks = seq(0, 100000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    

plot_argentina_interactive <- plot_ly(x = argentina$date, y = argentina$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")
plot_argentina_interactive2 <- plot_ly(x = argentina$date, y = argentina$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_argentina_interactive3 <- plot_ly(x = argentina$date, y = argentina$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")


plotly::subplot(plot_argentina_interactive,plot_argentina_interactive2,plot_argentina_interactive3, nrows=1 , margin= 0.05)

Austria - AT

austria <- vaccinations[grep("AUT", vaccinations$iso_code),]
austria_world_pop <- world_population[grep("Austria", world_population$Country),]

austria$population = austria_world_pop$Population
austria$percentage = (austria$people_vaccinated/austria$population)*100

plot_austria <- austria %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇦🇹") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_austria_1 = plot_austria+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_austria_1 = plot_austria_1 + ggtitle("Population Vaccinated on \n Austria (2021)")

plot_austria_percentage <- austria %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇦🇹") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_austria_2 = plot_austria_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_austria_2 = plot_austria_2 + ggtitle("Population (%) Vaccinated \n on Austria") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_austria_interactive <- plot_ly(x = austria$date, y = austria$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")
plot_austria_interactive2 <- plot_ly(x = austria$date, y = austria$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_austria_interactive3 <- plot_ly(x = austria$date, y = austria$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_austria_interactive,plot_austria_interactive2,plot_austria_interactive3, nrows=1 , margin= 0.05)

Bahrain - BH

bahrain <- vaccinations[grep("BHR", vaccinations$iso_code),]
bahrain_world_pop <- world_population[grep("Bahrain", world_population$Country),]

bahrain$population = bahrain_world_pop$Population
bahrain$percentage = (bahrain$people_vaccinated/bahrain$population)*100


bahrain$percentage[is.na(bahrain$percentage)] <- round(mean(bahrain$percentage, na.rm = TRUE))

plot_bahrain <- bahrain %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇧🇭") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_bahrain_1 = plot_bahrain+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_bahrain_1 = plot_bahrain_1 + ggtitle("Population (%) \n Vaccinated on \n Bahrain (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_bahrain_percentage <- bahrain %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇧🇭") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_bahrain_2 = plot_bahrain_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_bahrain_2 = plot_bahrain_2 + ggtitle("Population Vaccinated \n on Bahrain") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_bahrain_interactive <- plot_ly(x = bahrain$date, y = bahrain$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")
plot_bahrain_interactive2 <- plot_ly(x = bahrain$date, y = bahrain$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_bahrain_interactive3 <- plot_ly(x = bahrain$date, y = bahrain$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")


plotly::subplot(plot_bahrain_interactive,plot_bahrain_interactive2,plot_bahrain_interactive3, nrows=1 , margin = 0.05)

Belgium - BE

belgium <- vaccinations[grep("BEL", vaccinations$iso_code),]
belgium_world_pop <- world_population[grep("Belgium", world_population$Country),]

belgium$population = belgium_world_pop$Population
belgium$percentage = (belgium$people_vaccinated/belgium$population)*100
plot_belgium <- belgium %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇧🇪") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_belgium_1 = plot_belgium+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_belgium_1 = plot_belgium_1 + ggtitle("Population (%) \n Vaccinated on \n Belgium (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_belgium_percentage <- belgium %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇧🇪") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_belgium_2 = plot_belgium_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_belgium_2 = plot_belgium_2 + ggtitle("Population (%) Vaccinated \n on Belgium") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_belgium_interactive <- plot_ly(x = belgium$date, y = belgium$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_belgium_interactive2 <- plot_ly(x = belgium$date, y = belgium$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_belgium_interactive3 <- plot_ly(x = belgium$date, y = belgium$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_belgium_interactive,plot_belgium_interactive2,plot_belgium_interactive3, nrows=1 , margin = 0.05)

Brazil - BR

brazil <- vaccinations[grep("BRA", vaccinations$iso_code),]
brazil_world_pop <- world_population[grep("Brazil", world_population$Country),]

brazil$population = brazil_world_pop$Population
brazil$percentage = (brazil$people_vaccinated/brazil$population)*100
plot_brazil <- brazil %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇧🇷") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_brazil_1 = plot_brazil+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_brazil_1 = plot_brazil_1 + ggtitle("Population (%) \n Vaccinated on \n Brazil (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_brazil_percentage <- brazil %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇧🇷") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_brazil_2 = plot_brazil_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_brazil_2 = plot_brazil_2 + ggtitle("Population (%) Vaccinated \n on Brazil") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_brazil_interactive <- plot_ly(x = brazil$date, y = brazil$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_brazil_interactive2 <- plot_ly(x = brazil$date, y = brazil$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_brazil_interactive3 <- plot_ly(x = brazil$date, y = brazil$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_brazil_interactive,plot_brazil_interactive2,plot_brazil_interactive3, nrows=1 , margin = 0.05)

Bulgaria - BG

bulgaria <- vaccinations[grep("BGR", vaccinations$iso_code),]
bulgaria_world_pop <- world_population[grep("Bulgaria", world_population$Country),]

bulgaria$population = bulgaria_world_pop$Population
bulgaria$percentage = (bulgaria$people_vaccinated/bulgaria$population)*100
plot_bulgaria <- bulgaria %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇧🇬") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_bulgaria_1 = plot_bulgaria+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_bulgaria_1 = plot_bulgaria_1 + ggtitle("Population Vaccinated on  \n Bulgaria (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_bulgaria_percentage <- bulgaria %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇧🇬") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_bulgaria_2 = plot_bulgaria_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_bulgaria_2 = plot_bulgaria_2 + ggtitle("Population (%) Vaccinated \n on Bulgaria") + 
    theme(plot.title = element_text(size = 7, face = "bold"))
    
plot_bulgaria_interactive <- plot_ly(x = bulgaria$date, y = bulgaria$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_bulgaria_interactive2 <- plot_ly(x = bulgaria$date, y = bulgaria$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_bulgaria_interactive3 <- plot_ly(x = bulgaria$date, y = bulgaria$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_bulgaria_interactive,plot_bulgaria_interactive2,plot_bulgaria_interactive3, nrows=1 , margin=0.05)

Canada - CA

canada <- vaccinations[grep("CAN", vaccinations$iso_code),]
canada_world_pop <- world_population[grep("Canada", world_population$Country),]

canada$population = canada_world_pop$Population
canada$percentage = (canada$people_vaccinated/canada$population)*100
plot_canada <- canada %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇦") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_canada_1 = plot_canada+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_canada_1 = plot_canada_1 + ggtitle("Population (%) Vaccinated on Canada (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_canada_percentage <- canada %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇦") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_canada_2 = plot_canada_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_canada_1 = plot_canada_1 + ggtitle("Population Vaccinated \n on Canada") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_canada_interactive <- plot_ly(x = canada$date, y = canada$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_canada_interactive2 <- plot_ly(x = canada$date, y = canada$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_canada_interactive3 <- plot_ly(x = canada$date, y = canada$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_canada_interactive,plot_canada_interactive2,plot_canada_interactive3 ,nrows=1 , margin = 0.05)

Chile - CL

chile <- vaccinations[grep("CHL", vaccinations$iso_code),]
chile_world_pop <- world_population[grep("Chile", world_population$Country),]

chile$population = chile_world_pop$Population
chile$percentage = (chile$people_vaccinated/chile$population)*100
plot_chile<- chile %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇱") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_chile_1 = plot_chile+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_chile_1 = plot_chile_1 + ggtitle("Population Vaccinated on Chile (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_chile_percentage <- chile %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇱") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_chile_2 = plot_chile_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_chile_2 = plot_chile_2 + ggtitle("Population Vaccinated(%) \n  on Chile") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_chile_interactive <- plot_ly(x = chile$date, y = chile$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_chile_interactive2 <- plot_ly(x = chile$date, y = chile$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_chile_interactive3 <- plot_ly(x = chile$date, y = chile$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_chile_interactive,plot_chile_interactive2,plot_chile_interactive3, nrows=1 , margin = 0.05)

China - CN

china <- vaccinations[grep("CHN", vaccinations$iso_code),]
china_world_pop <- world_population[grep("China", world_population$Country),]

china$population = china_world_pop$Population
china$percentage = (china$people_vaccinated/china$population)*100
plot_china <- china %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇳") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_china_1 = plot_china+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_china_1 = plot_china_1 + ggtitle("Population Vaccinated on China (2020/21)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_china_percentage <- china %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇳") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_china_2 = plot_china_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_china_2 = plot_china_2 + ggtitle("Population (%) Vaccinated \n on China") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_china_interactive <- plot_ly(x = china$date, y = china$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_china_interactive2 <- plot_ly(x = china$date, y = china$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_china_interactive3 <- plot_ly(x = china$date, y = china$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_china_interactive,plot_china_interactive2,plot_china_interactive3, nrows=1 , margin = 0.05)

Costa Rica - CR

costa_rica <- vaccinations[grep("CRI", vaccinations$iso_code),]
costa_rica_world_pop <- world_population[grep("Costa Rica", world_population$Country),]

costa_rica$population = costa_rica_world_pop$Population
costa_rica$percentage = (costa_rica$people_vaccinated/costa_rica$population)*100



plot_costa_rica <- costa_rica %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇷") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_costa_rica_1 = plot_costa_rica+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_costa_rica_1 = plot_costa_rica_1 + ggtitle("Population (%) Vaccinated \n on Costa Rica") + 
    theme(plot.title = element_text(size = 6, face = "bold"))

plot_costa_rica_percentage <- costa_rica %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇷") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_costa_rica_2 = plot_costa_rica_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_costa_rica_2 = plot_costa_rica_2 + ggtitle("Population (%) Vaccinated \non  Costa Rica") + 
    theme(plot.title = element_text(size = 6, face = "bold"))

plot_costa_rica_interactive <- plot_ly(x = costa_rica$date, y = costa_rica$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_costa_rica_interactive2 <- plot_ly(x = costa_rica$date, y = costa_rica$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_costa_rica_interactive3 <- plot_ly(x = costa_rica$date, y = costa_rica$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_costa_rica_interactive,plot_costa_rica_interactive2,plot_costa_rica_interactive3, nrows=1 ,margin = 0.05)

Croatia - HR

croatia <- vaccinations[grep("HRV", vaccinations$iso_code),]
croatia_world_pop <- world_population[grep("Croatia", world_population$Country),]

croatia$population = croatia_world_pop$Population
croatia$percentage = (croatia$people_vaccinated/croatia$population)*100
plot_croatia <- croatia %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇭🇷") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_croatia_1 = plot_croatia+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_croatia_1 = plot_croatia_1 + ggtitle("Population Vaccinated \n on Croatia (2021)") + 
    theme(plot.title = element_text(size = 6, face = "bold"))

plot_croatia_percentage <- croatia %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇭🇷") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_croatia_2 = plot_croatia_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_croatia_2 = plot_croatia_2 + ggtitle("Population (%) Vaccinated on Croatia") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_croatia_interactive <- plot_ly(x = croatia$date, y = croatia$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_croatia_interactive2 <- plot_ly(x = croatia$date, y = croatia$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_croatia_interactive3 <- plot_ly(x = croatia$date, y = croatia$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_croatia_interactive,plot_croatia_interactive2,plot_croatia_interactive3, nrows=1)

Cyprus - CY

cyprus <- vaccinations[grep("CYP", vaccinations$iso_code),]
cyprus_world_pop <- world_population[grep("Cyprus", world_population$Country),]

cyprus$population = cyprus_world_pop$Population
cyprus$percentage = (cyprus$people_vaccinated/cyprus$population)*100
plot_cyprus <- cyprus %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇾") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_cyprus_1 = plot_cyprus+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_cyprus_1 = plot_cyprus_1 + ggtitle("Population Vaccinated \n on Cyprus (2021)") + 
    theme(plot.title = element_text(size = 6, face = "bold"))

plot_cyprus_percentage <- cyprus %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇾") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_cyprus_2 = plot_cyprus_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_cyprus_2 = plot_cyprus_2 + ggtitle("Population (%) Vaccinated on Cyprus") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_cyprus_interactive <- plot_ly(x = cyprus$date, y = cyprus$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_cyprus_interactive2 <- plot_ly(x = cyprus$date, y = cyprus$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_cyprus_interactive3 <- plot_ly(x = cyprus$date, y = cyprus$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_cyprus_interactive,plot_cyprus_interactive2,plot_cyprus_interactive3, nrows=1)

Czech Republic - CZ

czech_rep <- vaccinations[grep("CZE", vaccinations$iso_code),]
czech_rep_world_pop <- world_population[grep("Czech", world_population$Country),]

czech_rep$population = czech_rep_world_pop$Population
czech_rep$percentage = (czech_rep$people_vaccinated/czech_rep$population)*100

plot_czech_rep <- czech_rep %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇿") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_czech_rep_1 = plot_czech_rep+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_czech_rep_1 = plot_czech_rep_1 + ggtitle("Population Vaccinated \n on Czech Rep. (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_czech_rep_percentage <- czech_rep %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇿") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_czech_rep_2 = plot_czech_rep_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_czech_rep_2 = plot_czech_rep_2 + ggtitle("Population (%) Vaccinated \n on Czech Rep.") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_czech_rep_interactive <- plot_ly(x = czech_rep$date, y = czech_rep$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_czech_rep_interactive2 <- plot_ly(x = czech_rep$date, y = czech_rep$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_czech_rep_interactive3 <- plot_ly(x = czech_rep$date, y = czech_rep$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_czech_rep_interactive,plot_czech_rep_interactive2,plot_czech_rep_interactive3, nrows=1)

Denmark - DK

denmark <- vaccinations[grep("DNK", vaccinations$iso_code),]
denmark_world_pop <- world_population[grep("Denmark", world_population$Country),]

denmark$population = denmark_world_pop$Population
denmark$percentage = (denmark$people_vaccinated/denmark$population)*100
plot_denmark <- denmark %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇩🇰") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()
    
plot_denmark_1 = plot_denmark+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_denmark_1 = plot_denmark_1 + ggtitle("Population Vaccinated on Denmark (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_denmark_percentage <- denmark %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇩🇰") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_denmark_2 = plot_denmark_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_denmark_2 = plot_denmark_2 + ggtitle("Population (%) Vaccinated \n on Denmark") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_denmark_interactive <- plot_ly(x = denmark$date, y = denmark$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_denmark_interactive2 <- plot_ly(x = denmark$date, y = denmark$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_denmark_interactive3 <- plot_ly(x = denmark$date, y = denmark$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_denmark_interactive,plot_denmark_interactive2,plot_denmark_interactive3, nrows=1)

Estonia - EE

estonia <- vaccinations[grep("EST", vaccinations$iso_code),]
estonia_world_pop <- world_population[grep("Estonia", world_population$Country),]

estonia$population = estonia_world_pop$Population
estonia$percentage = (estonia$people_vaccinated/estonia$population)*100
plot_estonia <- estonia %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇪🇪") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_estonia_1 = plot_estonia+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_estonia_1 = plot_estonia_1 + ggtitle("Population Vaccinated \n on Estonia (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_estonia_percentage <- estonia %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇪🇪") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_estonia_2 = plot_estonia_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_estonia_2 = plot_estonia_2 + ggtitle("Population (%) Vaccinated \n on Estonia") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_estonia_interactive <- plot_ly(x = estonia$date, y = estonia$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_estonia_interactive2 <- plot_ly(x = estonia$date, y = estonia$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_estonia_interactive3 <- plot_ly(x = estonia$date, y = estonia$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_estonia_interactive,plot_estonia_interactive2,plot_estonia_interactive3, nrows=1)

Finland - FI

finland <- vaccinations[grep("FIN", vaccinations$iso_code),]
finland_world_pop <- world_population[grep("Finland", world_population$Country),]

finland$population = finland_world_pop$Population
finland$percentage = (finland$people_vaccinated/finland$population)*100
plot_finland <- finland %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇫🇮") +
    scale_y_continuous(breaks = seq(0, 100, 20),limits = c(0, 100))+
    theme_wsj()
    
plot_finland_1 = plot_finland+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_finland_1 = plot_finland_1 + ggtitle("Population (%) Vaccinated \n on Finland (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_finland_percentage <- finland %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇫🇮") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_finland_2 = plot_finland_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_finland_2 = plot_finland_2 + ggtitle("Population (%) Vaccinated\n on Finland") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_finland_interactive <- plot_ly(x = finland$date, y = finland$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_finland_interactive2 <- plot_ly(x = finland$date, y = finland$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_finland_interactive3 <- plot_ly(x = finland$date, y = finland$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_finland_interactive,plot_finland_interactive2,plot_finland_interactive3, nrows=1)

France - FR

france <- vaccinations[grep("FRA", vaccinations$iso_code),]
france_world_pop <- world_population[grep("France", world_population$Country),]

france$population = france_world_pop$Population
france$percentage = (france$people_vaccinated/france$population)*100
plot_france <- france %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇫🇷") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_france_1 = plot_france+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_france_1 = plot_france_1 + ggtitle("Population Vaccinated \n on France (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_france_percentage <- france %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇫🇷") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_france_2 = plot_france_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_france_2 = plot_france_2 + ggtitle("Population (%) Vaccinated \n on France") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_france_interactive <- plot_ly(x = france$date, y = france$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_france_interactive2 <- plot_ly(x = france$date, y = france$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_france_interactive3 <- plot_ly(x = france$date, y = france$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_france_interactive,plot_france_interactive2,plot_france_interactive3, nrows=1)

Germany - DE

germany <- vaccinations[grep("DEU", vaccinations$iso_code),]
germany_world_pop <- world_population[grep("Germany", world_population$Country),]

germany$population = germany_world_pop$Population
germany$percentage = (germany$people_vaccinated/germany$population)*100
plot_germany <- germany %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇩🇪") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_germany_1 = plot_germany+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_germany_1 = plot_germany_1 + ggtitle("Population Vaccinated \n on Germany (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_germany_percentage <- germany %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇩🇪") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_germany_2 = plot_germany_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_germany_2 = plot_germany_2 + ggtitle("Population (%) Vaccinated \n on Germany") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_germany_interactive <- plot_ly(x = germany$date, y = germany$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_germany_interactive2 <- plot_ly(x = germany$date, y = germany$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_germany_interactive3 <- plot_ly(x = germany$date, y = germany$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_germany_interactive,plot_germany_interactive2,plot_germany_interactive3, nrows=1)

Gibraltar - GI

gibraltar <- vaccinations[grep("GIB", vaccinations$iso_code),]
gibraltar_world_pop <- world_population[grep("Gibraltar", world_population$Country),]

gibraltar$population = gibraltar_world_pop$Population
gibraltar$percentage = (gibraltar$people_vaccinated/gibraltar$population)*100

plot_gibraltar <- gibraltar %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇬🇮") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_gibraltar_1 = plot_gibraltar+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_gibraltar_1 = plot_gibraltar_1 + ggtitle("Population Vaccinated on Gibraltar (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_gibraltar_percentage <- gibraltar %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇬🇮") +
    scale_y_continuous(breaks = seq(0, 40, 10),limits = c(0, 40))+
    theme_wsj()

plot_gibraltar_2 = plot_gibraltar_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_gibraltar_2 = plot_gibraltar_2 + ggtitle("Population (%) Vaccinated \n on Gibraltar") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_gibraltar_interactive <- plot_ly(x = gibraltar$date, y = gibraltar$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_gibraltar_interactive2 <- plot_ly(x = gibraltar$date, y = gibraltar$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_gibraltar_interactive3 <- plot_ly(x = gibraltar$date, y = gibraltar$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_gibraltar_interactive,plot_gibraltar_interactive2,plot_gibraltar_interactive3, nrows=1)

Greece - GR

greece <- vaccinations[grep("GRC", vaccinations$iso_code),]
greece_world_pop <- world_population[grep("Greece", world_population$Country),]

greece$population = greece_world_pop$Population
greece$percentage = (greece$people_vaccinated/greece$population)*100

plot_greece <- greece %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇬🇷") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_greece_1 = plot_greece+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_greece_1 = plot_greece_1 + ggtitle("Population Vaccinated \n on Greece (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_greece_percentage <- greece %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇬🇷") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_greece_2 = plot_greece_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_greece_2 = plot_greece_2 + ggtitle("Population (%) Vaccinated \n on Greece") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_greece_interactive <- plot_ly(x = greece$date, y = greece$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_greece_interactive2 <- plot_ly(x = greece$date, y = greece$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_greece_interactive3 <- plot_ly(x = greece$date, y = greece$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_greece_interactive,plot_greece_interactive2,plot_greece_interactive3, nrows=1)

Hungary - HU

hungary <- vaccinations[grep("HUN", vaccinations$iso_code),]
hungary_world_pop <- world_population[grep("Hungary", world_population$Country),]

hungary$population = hungary_world_pop$Population
hungary$percentage = (hungary$people_vaccinated/hungary$population)*100

plot_hungary <- hungary %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇭🇺") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_hungary_1 = plot_hungary + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_hungary_1 = plot_hungary_1 + ggtitle("Population (%) Vaccinated \n on Hungary (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_hungary_percentage <- hungary %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇭🇺") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_hungary_2 = plot_hungary_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_hungary_2 = plot_hungary_2 + ggtitle("Population (%) Vaccinated \n on Hungary") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_hungary_interactive <- plot_ly(x = hungary$date, y = hungary$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_hungary_interactive2 <- plot_ly(x = hungary$date, y = hungary$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_hungary_interactive3 <- plot_ly(x = hungary$date, y = hungary$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_hungary_interactive,plot_hungary_interactive2, plot_hungary_interactive3,nrows=1)

Iceland - IS

iceland <- vaccinations[grep("ISL", vaccinations$iso_code),]
iceland_world_pop <- world_population[grep("Iceland", world_population$Country),]

iceland$population = iceland_world_pop$Population
iceland$percentage = (iceland$people_vaccinated/iceland$population)*100

plot_iceland <- iceland %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇸") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_iceland_1 = plot_iceland + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_iceland_1 = plot_iceland_1 + ggtitle("Population Vaccinated on Iceland (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_iceland_percentage <- iceland %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇸") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_iceland_2 = plot_iceland_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_iceland_2 = plot_iceland_2 + ggtitle("Population (%) Vaccinated \n on Iceland") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_iceland_interactive <- plot_ly(x = iceland$date, y = iceland$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_iceland_interactive2 <- plot_ly(x = iceland$date, y = iceland$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_iceland_interactive3 <- plot_ly(x = iceland$date, y = iceland$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_iceland_interactive,plot_iceland_interactive2,plot_iceland_interactive3 ,nrows=1)

India - IN

india <- vaccinations[grep("IND", vaccinations$iso_code),]
india_world_pop <- world_population[grep("India", world_population$Country),]

india$population = india_world_pop$Population
india$percentage = (india$people_vaccinated/india$population)*100


plot_india <- india %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇳") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_india_1 = plot_india + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_india_1 = plot_india_1 + ggtitle("Population Vaccinated on India (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_india_percentage <- india %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇳") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_india_2 = plot_india_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_india_2 = plot_india_2 + ggtitle("Population (%) Vaccinated \n on India (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_india_interactive <- plot_ly(x = india$date, y = india$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_india_interactive2 <- plot_ly(x = india$date, y = india$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_india_interactive3 <- plot_ly(x = india$date, y = india$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_india_interactive,plot_india_interactive2,plot_india_interactive3, nrows=1)

Indonesia - ID

indonesia <- vaccinations[grep("IDN", vaccinations$iso_code),]
indonesia_world_pop <- world_population[grep("Indonesia", world_population$Country),]

indonesia$population = indonesia_world_pop$Population
indonesia$percentage = (indonesia$people_vaccinated/indonesia$population)*100


plot_indonesia <- indonesia %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇩") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_indonesia_1 = plot_indonesia + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_indonesia_1 = plot_indonesia_1 + ggtitle("Population Vaccinated on Indonesia (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_indonesia_percentage <- indonesia %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇩") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_indonesia_2 = plot_indonesia_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_indonesia_2 = plot_indonesia_2 + ggtitle("Population (%) Vaccinated \n on Indonesia (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_indonesia_interactive <- plot_ly(x = indonesia$date, y = indonesia$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_indonesia_interactive2 <- plot_ly(x = indonesia$date, y = indonesia$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_indonesia_interactive3 <- plot_ly(x = indonesia$date, y = indonesia$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_indonesia_interactive,plot_indonesia_interactive2,plot_indonesia_interactive3, nrows=1)

NA

Ireland - IE

ireland <- vaccinations[grep("IRL", vaccinations$iso_code),]
ireland_world_pop <- world_population[grep("Ireland", world_population$Country),]

ireland$population = ireland_world_pop$Population
ireland$percentage = (ireland$people_vaccinated/ireland$population)*100


plot_ireland <- ireland %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇪") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_ireland_1 = plot_ireland + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_ireland_1 = plot_ireland_1 + ggtitle("Population Vaccinated \n on Ireland (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_ireland_percentage <- ireland %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇪") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_ireland_2 = plot_ireland_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_ireland_2 = plot_ireland_2 + ggtitle("Population (%) Vaccinated \n on Ireland (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_ireland_interactive <- plot_ly(x = ireland$date, y = ireland$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_ireland_interactive2 <- plot_ly(x = ireland$date, y = ireland$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_ireland_interactive3 <- plot_ly(x = ireland$date, y = ireland$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_ireland_interactive,plot_ireland_interactive2,plot_ireland_interactive3, nrows=1)

Israel - IL

israel <- vaccinations[grep("ISR", vaccinations$iso_code),]
israel_world_pop <- world_population[grep("Israel", world_population$Country),]

israel$population = israel_world_pop$Population
israel$percentage = (israel$people_vaccinated/israel$population)*100


plot_israel <- israel %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇱") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_israel_1 = plot_israel + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_israel_1 = plot_israel_1 + ggtitle("Population Vaccinated \n on Israel (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_israel_percentage <- israel %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇱") +
    scale_y_continuous(breaks = seq(0, 40, 5),limits = c(0, 40))+
    theme_wsj()

plot_israel_2 = plot_israel_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_israel_2 = plot_israel_2 + ggtitle("Population (%) Vaccinated \n on Israel (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_israel_interactive <- plot_ly(x = israel$date, y = israel$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_israel_interactive2 <- plot_ly(x = israel$date, y = israel$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_israel_interactive3 <- plot_ly(x = israel$date, y = israel$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_israel_interactive,plot_israel_interactive2,plot_israel_interactive3, nrows=1)

Italy - IT

italy <- vaccinations[grep("ITA", vaccinations$iso_code),]
italy_world_pop <- world_population[grep("Italy", world_population$Country),]

italy$population = italy_world_pop$Population
italy$percentage = (italy$people_vaccinated/italy$population)*100


plot_italy <- italy %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇹") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_italy_1 = plot_italy + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_italy_1 = plot_italy_1 + ggtitle("Population Vaccinated \n on Italy(2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_italy_percentage <- italy %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇹") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_italy_2 = plot_italy_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_italy_2 = plot_italy_2 + ggtitle("Population (%) Vaccinated \n on Italy (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_italy_interactive <- plot_ly(x = italy$date, y = italy$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_italy_interactive2 <- plot_ly(x = italy$date, y = italy$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_italy_interactive3 <- plot_ly(x = italy$date, y = italy$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_italy_interactive,plot_italy_interactive2,plot_italy_interactive3, nrows=1)

Kuwait - KW

kuwait <- vaccinations[grep("KWT", vaccinations$iso_code),]
kuwait_world_pop <- world_population[grep("Kuwait", world_population$Country),]

kuwait$population = kuwait_world_pop$Population
kuwait$percentage = (kuwait$people_vaccinated/kuwait$population)*100


plot_kuwait <- kuwait %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇰🇼") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_kuwait_1 = plot_kuwait + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_kuwait_1 = plot_kuwait_1 + ggtitle("Population Vaccinated \n on Kuwait(2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_kuwait_percentage <- kuwait %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇰🇼") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_kuwait_2 = plot_kuwait_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_kuwait_2 = plot_kuwait_2 + ggtitle("Population (%) Vaccinated \n on Kuwait (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_kuwait_interactive <- plot_ly(x = kuwait$date, y = kuwait$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_kuwait_interactive2 <- plot_ly(x = kuwait$date, y = kuwait$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_kuwait_interactive3 <- plot_ly(x = kuwait$date, y = kuwait$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_kuwait_interactive,plot_kuwait_interactive2,plot_kuwait_interactive3, nrows=1)

Latvia - LV

latvia <- vaccinations[grep("LVA", vaccinations$iso_code),]
latvia_world_pop <- world_population[grep("Latvia", world_population$Country),]

latvia$population = latvia_world_pop$Population
latvia$percentage = (latvia$people_vaccinated/latvia$population)*100


plot_latvia <- latvia %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇱🇻") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_latvia_1 = plot_latvia + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_latvia_1 = plot_latvia_1 + ggtitle("Population Vaccinated \n on Italy (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_latvia_percentage <- latvia %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇱🇻") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_latvia_2 = plot_latvia_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_latvia_2 = plot_latvia_2 + ggtitle("Population (%) Vaccinated \n on Latvia ") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_latvia_interactive <- plot_ly(x = latvia$date, y = latvia$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_latvia_interactive2 <- plot_ly(x = latvia$date, y = latvia$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_latvia_interactive3 <- plot_ly(x = latvia$date, y = latvia$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_latvia_interactive,plot_latvia_interactive2,plot_latvia_interactive3, nrows=1)

Lithuania - LT

lithuania <- vaccinations[grep("LTU", vaccinations$iso_code),]
lithuania_world_pop <- world_population[grep("Lithuania", world_population$Country),]

lithuania$population = lithuania_world_pop$Population
lithuania$percentage = (lithuania$people_vaccinated/lithuania$population)*100


plot_lithuania <- lithuania %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇱🇹") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_lithuania_1 = plot_lithuania + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_lithuania_1 = plot_lithuania_1 + ggtitle("Population Vaccinated \n on Lithuania ") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_lithuania_percentage <- lithuania %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇱🇹") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_lithuania_2 = plot_lithuania_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_lithuania_2 = plot_lithuania_2 + ggtitle("Population (%) Vaccinated \n on Lithuania (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_lithuania_interactive <- plot_ly(x = lithuania$date, y = lithuania$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_lithuania_interactive2 <- plot_ly(x = lithuania$date, y = lithuania$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_lithuania_interactive3 <- plot_ly(x = lithuania$date, y = lithuania$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_lithuania_interactive,plot_lithuania_interactive2,plot_lithuania_interactive3, nrows=1)

Luxemberg - LU

luxembourg <- vaccinations[grep("LUX", vaccinations$iso_code),]
luxembourg_world_pop <- world_population[grep("Luxembourg", world_population$Country),]

luxembourg$population = luxembourg_world_pop$Population
luxembourg$percentage = (luxembourg$people_vaccinated/luxembourg$population)*100


plot_luxembourg <- luxembourg %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇱🇺") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_luxembourg_1 = plot_luxembourg + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_luxembourg_1 = plot_luxembourg_1 + ggtitle("Population Vaccinated \n on Luxembourg") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_luxembourg_percentage <- italy %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇱🇺") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_luxembourg_2 = plot_luxembourg_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_luxembourg_2 = plot_luxembourg_2 + ggtitle("Population (%) Vaccinated \n on Luxembourg") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_luxembourg_interactive <- plot_ly(x = luxembourg$date, y = luxembourg$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_luxembourg_interactive2 <- plot_ly(x = luxembourg$date, y = luxembourg$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_luxembourg_interactive3 <- plot_ly(x = luxembourg$date, y = luxembourg$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_luxembourg_interactive,plot_luxembourg_interactive2,plot_luxembourg_interactive3, nrows=1)

Malta - MT

malta <- vaccinations[grep("MLT", vaccinations$iso_code),]
malta_world_pop <- world_population[grep("Malta", world_population$Country),]

malta$population = malta_world_pop$Population
malta$percentage = (malta$people_vaccinated/malta$population)*100


plot_malta <- malta %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇲🇹") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_malta_1 = plot_malta + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_malta_1 = plot_malta_1 + ggtitle("Population Vaccinated \n on Malta (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_malta_percentage <- malta %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇲🇹") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_malta_2 = plot_malta_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_malta_2 = plot_malta_2 + ggtitle("Population (%) Vaccinated \n on Malta") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_malta_interactive <- plot_ly(x = malta$date, y = malta$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_malta_interactive2 <- plot_ly(x = malta$date, y = malta$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_malta_interactive3 <- plot_ly(x = malta$date, y = malta$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_malta_interactive,plot_malta_interactive2,plot_malta_interactive3, nrows=1)

Mexico - MX

mexico <- vaccinations[grep("MEX", vaccinations$iso_code),]
mexico_world_pop <- world_population[grep("Mexico", world_population$Country),]

mexico$population = mexico_world_pop$Population
mexico$percentage = (mexico$people_vaccinated/mexico$population)*100


plot_mexico <- mexico %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇲🇽") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_mexico_1 = plot_mexico + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_mexico_1 = plot_mexico_1 + ggtitle("Population Vaccinated \n on Mexico ") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_mexico_percentage <- mexico %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇲🇽") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_mexico_2 = plot_mexico_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_mexico_2 = plot_mexico_2 + ggtitle("Population (%) Vaccinated \n on Mexico (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_mexico_interactive <- plot_ly(x = mexico$date, y = mexico$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_mexico_interactive2 <- plot_ly(x = mexico$date, y = mexico$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_mexico_interactive3 <- plot_ly(x = mexico$date, y = mexico$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_mexico_interactive,plot_mexico_interactive2,plot_mexico_interactive3, nrows=1)

Norway - NO

norway <- vaccinations[grep("NOR", vaccinations$iso_code),]
norway_world_pop <- world_population[grep("Norway", world_population$Country),]

norway$population = norway_world_pop$Population
norway$percentage = (norway$people_vaccinated/norway$population)*100


plot_norway <- norway %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇳🇴") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_norway_1 = plot_norway + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_norway_1 = plot_norway_1 + ggtitle("Population Vaccinated \n on Norway (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_norway_percentage <- norway %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇳🇴") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_norway_2 = plot_norway_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_norway_2 = plot_norway_2 + ggtitle("Population (%) Vaccinated \n on Morway (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_norway_interactive <- plot_ly(x = norway$date, y = norway$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_norway_interactive2 <- plot_ly(x = norway$date, y = norway$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_norway_interactive3 <- plot_ly(x = norway$date, y = norway$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_norway_interactive,plot_norway_interactive2,plot_norway_interactive3, nrows=1)

Oman - OM

oman <- vaccinations[grep("OMN", vaccinations$iso_code),]
oman_world_pop <- world_population[grep("Oman", world_population$Country),]

oman$population = oman_world_pop$Population
oman$percentage = (oman$people_vaccinated/oman$population)*100


plot_oman <- oman %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇴🇲") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_oman_1 = plot_oman + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_oman_1 = plot_oman_1 + ggtitle("Population Vaccinated \n on Oman(2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_oman_percentage <- oman %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇴🇲") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_oman_2 = plot_oman_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_oman_2 = plot_oman_2 + ggtitle("Population (%) Vaccinated \n on Oman (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_oman_interactive <- plot_ly(x = oman$date, y = oman$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_oman_interactive2 <- plot_ly(x = oman$date, y = oman$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_oman_interactive3 <- plot_ly(x = oman$date, y = oman$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_oman_interactive,plot_oman_interactive2,plot_oman_interactive3, nrows=1)

Panama - PA

panama <- vaccinations[grep("PAN", vaccinations$iso_code),]
panama_world_pop <- world_population[grep("Panama", world_population$Country),]

panama$population = panama_world_pop$Population
panama$percentage = (panama$people_vaccinated/panama$population)*100


plot_panama <- panama %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇵🇦") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_panama_1 = plot_panama + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_panama_1 = plot_panama_1 + ggtitle("Population  Vaccinated \n on Panama (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_panama_percentage <- panama %>%
  ggplot( aes(x=date, y = percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇵🇦") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_panama_2 = plot_panama_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_panama_2 = plot_panama_2 + ggtitle("Population (%) Vaccinated \n on Panama (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_panama_interactive <- plot_ly(x = panama$date, y = panama$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_panama_interactive2 <- plot_ly(x = panama$date, y = panama$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_panama_interactive3 <- plot_ly(x = panama$date, y = panama$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_panama_interactive,plot_panama_interactive2,plot_panama_interactive3, nrows=1)

Poland - PL

poland <- vaccinations[grep("POL", vaccinations$iso_code),]
poland_world_pop <- world_population[grep("Poland", world_population$Country),]

poland$population = poland_world_pop$Population
poland$percentage = (poland$people_vaccinated/poland$population)*100


plot_poland <- poland %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇵🇱") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_poland_1 = plot_poland + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_poland_1 = plot_poland_1 + ggtitle("Population  Vaccinated \n on Poland (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_poland_percentage <- poland %>%
  ggplot( aes(x=date, y = percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇵🇱") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_poland_2 = plot_poland_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_poland_2 = plot_poland_2 + ggtitle("Population (%) Vaccinated \n on Poland (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_poland_interactive <- plot_ly(x = poland$date, y = poland$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_poland_interactive2 <- plot_ly(x = poland$date, y = poland$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_poland_interactive3 <- plot_ly(x = poland$date, y = poland$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_poland_interactive,plot_poland_interactive2,plot_poland_interactive3, nrows=1)

Portugal - PT

portugal <- vaccinations[grep("PRT", vaccinations$iso_code),]
portugal_world_pop <- world_population[grep("Portugal", world_population$Country),]

portugal$population = portugal_world_pop$Population
portugal$percentage = (portugal$people_vaccinated/portugal$population)*100


plot_portugal <- portugal %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇵🇹") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_portugal_1 = plot_portugal + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_portugal_1 = plot_portugal_1 + ggtitle("Population Vaccinated \n on Portugal (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_portugal_percentage <- portugal %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇵🇹") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_portugal_2 = plot_portugal_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_portugal_2 = plot_portugal_2 + ggtitle("Population (%) Vaccinated \n on Portugal (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_portugal_interactive <- plot_ly(x = portugal$date, y = portugal$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_portugal_interactive2 <- plot_ly(x = portugal$date, y = portugal$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_portugal_interactive3 <- plot_ly(x = portugal$date, y = portugal$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_portugal_interactive,plot_portugal_interactive2,plot_portugal_interactive3, nrows=1)

Romania - RO

romania <- vaccinations[grep("ROU", vaccinations$iso_code),]
romania_world_pop <- world_population[grep("Romania", world_population$Country),]

romania$population = romania_world_pop$Population
romania$percentage = (romania$people_vaccinated/romania$population)*100


plot_romania <- romania %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇷🇴") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_romania_1 = plot_romania + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_romania_1 = plot_romania_1 + ggtitle("Vaccination on \n Romania(2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_romania_percentage <- romania %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇷🇴") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_romania_2 = plot_romania_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_romania_2 = plot_romania_2 + ggtitle("Vaccination on \n Romania") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_romania_interactive <- plot_ly(x = romania$date, y = romania$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_romania_interactive2 <- plot_ly(x = romania$date, y = romania$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_romania_interactive3 <- plot_ly(x = romania$date, y = romania$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_romania_interactive,plot_romania_interactive2,plot_romania_interactive3, nrows=1)

Russia - RU

russia <- vaccinations[grep("RUS", vaccinations$iso_code),]
russia_world_pop <- world_population[grep("Russia", world_population$Country),]

russia$population = russia_world_pop$Population
russia$percentage = (russia$people_vaccinated/russia$population)*100


plot_russia <- russia %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇷🇺") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_russia_1 = plot_russia + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_russia_1 = plot_russia_1 + ggtitle("Vaccination on \n Russia(2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_russia_percentage <- russia %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇷🇺") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_russia_2 = plot_russia_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_russia_2 = plot_russia_2 + ggtitle("Vaccination on \n Russia") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_russia_interactive <- plot_ly(x = russia$date, y = russia$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_russia_interactive2 <- plot_ly(x = russia$date, y = russia$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_russia_interactive3 <- plot_ly(x = russia$date, y = russia$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_russia_interactive,plot_russia_interactive2,plot_russia_interactive3, nrows=1)

Saudi Arabia - SA

saudi_arabia <- vaccinations[grep("SAU", vaccinations$iso_code),]
saudi_arabia_world_pop <- world_population[grep("Saudi Arabia", world_population$Country),]

saudi_arabia$population = saudi_arabia_world_pop$Population
saudi_arabia$percentage = (saudi_arabia$people_vaccinated/saudi_arabia$population)*100


plot_saudi_arabia <- saudi_arabia %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇸🇦") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_saudi_arabia_1 = plot_saudi_arabia + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_saudi_arabia_1 = plot_saudi_arabia_1 + ggtitle("Vaccination on \n Saudi Arabia (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_saudi_arabia_percentage <- saudi_arabia %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇸🇦") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_saudi_arabia_2 = plot_saudi_arabia_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_saudi_arabia_2 = plot_saudi_arabia_2 + ggtitle("Vaccination on \n Saudi Arabia (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_saudi_arabia_interactive <- plot_ly(x = saudi_arabia$date, y = saudi_arabia$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_saudi_arabia_interactive2 <- plot_ly(x = saudi_arabia$date, y = saudi_arabia$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_saudi_arabia_interactive3 <- plot_ly(x = saudi_arabia$date, y = saudi_arabia$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_saudi_arabia_interactive,plot_saudi_arabia_interactive2,plot_saudi_arabia_interactive3, nrows=1)

Serbia - RS

serbia <- vaccinations[grep("SRB", vaccinations$iso_code),]
serbia_world_pop <- world_population[grep("Serbia", world_population$Country),]

serbia$population = serbia_world_pop$Population
serbia$percentage = (serbia$people_vaccinated/serbia$population)*100


plot_serbia <- serbia %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇷🇸") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_serbia_1 = plot_serbia + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_serbia_1 = plot_serbia_1 + ggtitle("Vaccination on \n Serbia (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_serbia_percentage <- serbia %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇷🇸") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_serbia_2 = plot_serbia_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_serbia_2 = plot_serbia_2 + ggtitle("Vaccination on \n Serbia (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_serbia_interactive <- plot_ly(x = serbia$date, y = serbia$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_serbia_interactive2 <- plot_ly(x = serbia$date, y = serbia$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_serbia_interactive3 <- plot_ly(x = serbia$date, y = serbia$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_serbia_interactive,plot_serbia_interactive2,plot_serbia_interactive3, nrows=1)

Seychelles - SC

seychelles <- vaccinations[grep("SYC", vaccinations$iso_code),]
seychelles_world_pop <- world_population[grep("Seychelles", world_population$Country),]

seychelles$population = seychelles_world_pop$Population
seychelles$percentage = (seychelles$people_vaccinated/seychelles$population)*100


plot_seychelles <- seychelles %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇸🇨") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_seychelles_1 = plot_seychelles + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_seychelles_1 = plot_seychelles_1 + ggtitle("Vaccination on \n Seychelles (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_seychelles_percentage <- seychelles %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇸🇨") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_seychelles_2 = plot_seychelles_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_seychelles_2 = plot_seychelles_2 + ggtitle("Vaccination on \n Seychelles (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_seychelles_interactive <- plot_ly(x = seychelles$date, y = seychelles$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_seychelles_interactive2 <- plot_ly(x = seychelles$date, y = seychelles$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_seychelles_interactive3 <- plot_ly(x = seychelles$date, y = seychelles$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_seychelles_interactive,plot_seychelles_interactive2,plot_seychelles_interactive3, nrows=1)

Slovakia - SK

slovakia <- vaccinations[grep("SVK", vaccinations$iso_code),]
slovakia_world_pop <- world_population[grep("Slovakia", world_population$Country),]

slovakia$population = slovakia_world_pop$Population
slovakia$percentage = (slovakia$people_vaccinated/slovakia$population)*100


plot_slovakia <- slovakia %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇸🇰") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_slovakia_1 = plot_slovakia + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_slovakia_1 = plot_slovakia_1 + ggtitle("Vaccination on \n Slovakia (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_slovakia_percentage <- slovakia %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇸🇰") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_slovakia_2 = plot_slovakia_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_slovakia_2 = plot_slovakia_2 + ggtitle("Vaccination on \n Slovakia (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_slovakia_interactive <- plot_ly(x = slovakia$date, y = slovakia$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_slovakia_interactive2 <- plot_ly(x = slovakia$date, y = slovakia$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_slovakia_interactive3 <- plot_ly(x = slovakia$date, y = slovakia$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_slovakia_interactive,plot_slovakia_interactive2,plot_slovakia_interactive3 , nrows=1)

Slovenia - SI

slovenia <- vaccinations[grep("SVN", vaccinations$iso_code),]
slovenia_world_pop <- world_population[grep("Slovenia", world_population$Country),]

slovenia$population = slovenia_world_pop$Population
slovenia$percentage = (slovenia$people_vaccinated/slovenia$population)*100


plot_slovenia <- slovenia %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇸🇮") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_slovenia_1 = plot_slovenia + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_slovenia_1 = plot_slovenia_1 + ggtitle("Vaccination on \n Slovenia (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_slovenia_percentage <- slovenia %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇸🇮") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_slovenia_2 = plot_slovenia_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_slovenia_2 = plot_slovenia_2 + ggtitle("Vaccination on \n Slovenia (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_slovenia_interactive <- plot_ly(x = slovenia$date, y = slovenia$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_slovenia_interactive2 <- plot_ly(x = slovenia$date, y = slovenia$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_slovenia_interactive3 <- plot_ly(x = slovenia$date, y = slovenia$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_slovenia_interactive,plot_slovenia_interactive2,plot_slovenia_interactive3, nrows=1)

Spain - ES

spain <- vaccinations[grep("ESP", vaccinations$iso_code),]
spain_world_pop <- world_population[grep("Spain", world_population$Country),]

spain$population = spain_world_pop$Population
spain$percentage = (spain$people_vaccinated/spain$population)*100


plot_spain <- spain %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇪🇸") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_spain_1 = plot_spain + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_spain_1 = plot_spain_1 + ggtitle("Vaccination on \n Spain (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_spain_percentage <- spain %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇪🇸") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_spain_2 = plot_spain_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_spain_2 = plot_spain_2 + ggtitle("Vaccination on \n Spain (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_spain_interactive <- plot_ly(x = spain$date, y = spain$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_spain_interactive2 <- plot_ly(x = spain$date, y = spain$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_spain_interactive3 <- plot_ly(x = spain$date, y = spain$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_spain_interactive,plot_spain_interactive2,plot_spain_interactive3, nrows=1)

Sweden - SE

sweden <- vaccinations[grep("SWE", vaccinations$iso_code),]
sweden_world_pop <- world_population[grep("Sweden", world_population$Country),]

sweden$population = sweden_world_pop$Population
sweden$percentage = (sweden$people_vaccinated/sweden$population)*100


plot_sweden <- sweden %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇸🇪") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_sweden_1 = plot_sweden + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))

Turkey - TR

turkey <- vaccinations[grep("TUR", vaccinations$iso_code),]
turkey_world_pop <- world_population[grep("Turkey", world_population$Country),]

turkey$population = turkey_world_pop$Population
turkey$percentage = (turkey$people_vaccinated/turkey$population)*100


plot_turkey <- turkey %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇹🇷") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_turkey_1 = plot_turkey + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_turkey_1 = plot_turkey_1 + ggtitle("Vaccination on \n Turkey (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_turkey_percentage <- turkey %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇹🇷") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_turkey_2 = plot_turkey_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_turkey_2 = plot_turkey_2 + ggtitle("Vaccination on \n Turkey (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_turkey_interactive <- plot_ly(x = turkey$date, y = turkey$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_turkey_interactive2 <- plot_ly(x = turkey$date, y = turkey$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_turkey_interactive3 <- plot_ly(x = turkey$date, y = turkey$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_turkey_interactive,plot_turkey_interactive2,plot_turkey_interactive3, nrows=1)

United Arab Emirates - AE

uae <- vaccinations[grep("ARE", vaccinations$iso_code),]
uae_world_pop <- world_population[grep("United Arab Emirates", world_population$Country),]

uae$population = uae_world_pop$Population
uae$percentage = (uae$people_vaccinated/uae$population)*100


plot_uae <- uae %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇦🇪") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_uae_1 = plot_uae + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_uae_1 = plot_uae_1 + ggtitle("Vaccination on \n UAE (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_uae_percentage <- uae %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇦🇪") +
    scale_y_continuous(breaks = seq(0, 40, 5),limits = c(0, 40))+
    theme_wsj()

plot_uae_2 = plot_uae_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_uae_2 = plot_uae_2 + ggtitle("Vaccination on \n UAE (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_uae_interactive <- plot_ly(x = uae$date, y = uae$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_uae_interactive2 <- plot_ly(x = uae$date, y = uae$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_uae_interactive3 <- plot_ly(x = uae$date, y = uae$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_uae_interactive,plot_uae_interactive2,plot_uae_interactive3, nrows=1)

United Kingdom - UK

uk <- vaccinations[grep("GBR", vaccinations$iso_code),]
uk_world_pop <- world_population[grep("United Kingdom", world_population$Country),]

uk$population = uk_world_pop$Population
uk$percentage = (uk$people_vaccinated/uk$population)*100


plot_uk <- uk %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇬🇧") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_uk_1 = plot_uk + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_uk_1 = plot_uk_1 + ggtitle("Vaccination on \n United Kingdom (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_uk_percentage <- uk %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇬🇧") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_uk_2 = plot_uk_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_uk_2 = plot_uk_2 + ggtitle("Vaccination on \n UK (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_uk_interactive <- plot_ly(x = uk$date, y = uk$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_uk_interactive2 <- plot_ly(x = uk$date, y = uk$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_uk_interactive3 <- plot_ly(x = uk$date, y = uk$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_uk_interactive,plot_uk_interactive2,plot_uk_interactive3 , nrows=1)

United States of America - US

usa <- vaccinations[grep("USA", vaccinations$iso_code),]
usa_world_pop <- world_population[grep("United States", world_population$Country),]

usa$population = usa_world_pop$Population
usa$percentage = (usa$people_vaccinated/usa$population)*100


plot_usa <- usa %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇺🇸") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_usa_1 = plot_usa + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_usa_1 = plot_usa_1 + ggtitle("Vaccination on \n USA (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_usa_percentage <- usa %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇺🇸") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_usa_2 = plot_usa_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_usa_2 = plot_usa_2 + ggtitle("Vaccination on \n USA (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_usa_interactive <- plot_ly(x = usa$date, y = usa$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_usa_interactive2 <- plot_ly(x = usa$date, y = usa$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_usa_interactive3 <- plot_ly(x = usa$date, y = usa$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_usa_interactive,plot_usa_interactive2,plot_usa_interactive3, nrows=1)

Summarizing Plots

Percentage of population vaccinated

x1 = grid.arrange(plot_argentina_2,plot_austria_2,plot_bahrain_2,plot_belgium_2,plot_bulgaria_2, plot_chile_2,nrow=2)

x2 = grid.arrange(plot_china_2 , plot_costa_rica_2, plot_croatia_2, plot_czech_rep_2, plot_denmark_2, plot_estonia_2,nrow=2)

x3 = grid.arrange(plot_finland_2 , plot_france_2 , plot_germany_2,plot_gibraltar_2 , plot_greece_2, plot_hungary_2 , nrow = 2)

x4 = grid.arrange(plot_iceland_2 , plot_india_2 , plot_ireland_2,plot_israel_2 , plot_italy_2, plot_latvia_2 , nrow = 2)

x5 = grid.arrange(plot_lithuania_2 , plot_luxembourg_2 , plot_malta_2,plot_mexico_2 , plot_norway_2, plot_oman_2, nrow = 2)

x6 = grid.arrange(plot_poland_2 , plot_portugal_2 , plot_romania_2,plot_saudi_arabia_2, plot_serbia_2, plot_seychelles_1 , nrow = 2)

x7 = grid.arrange(plot_slovakia_2 , plot_slovenia_2 , plot_spain_2,plot_sweden_2 , plot_turkey_2, plot_uae_2 , plot_uk_2 , plot_usa_2, nrow = 2)

Less Vaccinated Country in terms of Population (% of Population)

vaccinations1 = vaccinations
vaccinations1$percentage[vaccinations$country == "Argentina"] <- max(argentina$percentage , na.rm = TRUE)
vaccinations1$percentage[vaccinations$country == "Austria"] <- max(austria$percentage , na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Bahrain"] <- max(bahrain$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Belgium"] <- max(belgium$percentage, na.rm = TRUE)
vaccinations1$percentage[vaccinations$country == "Brazil"] <- max(brazil$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Bulgaria"] <- max(bulgaria$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Canada"] <- max(canada$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Chile"] <- max(chile$percentage, na.rm = TRUE)
vaccinations1$percentage[vaccinations$country == "China"] <- max(china$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Costa Rica"] <- max(costa_rica$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Croatia"] <- max(croatia$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Cyprus"] <- max(cyprus$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Czech"] <- max(czech_rep$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Denmark"] <- max(denmark$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Estonia"] <- max(estonia$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Finland"] <- max(finland$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "France"] <- max(france$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Germany"] <- max(germany$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Gibraltar"] <- max(gibraltar$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Greece"] <- max(greece$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Hungary"] <- max(hungary$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Iceland"] <- max(iceland$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "India"] <- max(india$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Indonesia"] <- max(indonesia$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Ireland"] <- max(ireland$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Israel"] <- max(israel$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Italy"] <- max(italy$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Kuwait"] <- max(kuwait$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Latvia"] <- max(latvia$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Lithuania"] <- max(lithuania$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Luxembourg"] <- max(luxembourg$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Malta"] <- max(malta$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Mexico"] <- max(mexico$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Norway"] <- max(norway$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Oman"] <- max(oman$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Panama"] <- max(panama$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Poland"] <- max(poland$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Portugal"] <- max(portugal$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Romania"] <- max(romania$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Russia"] <- max(russia$percentage, na.rm = TRUE)
vaccinations1$percentage[vaccinations$country == "Saudi Arabia"] <- max(saudi_arabia$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Serbia"] <- max(serbia$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Seychelles"] <- max(seychelles$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Slovakia"] <- max(slovakia$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Slovenia"] <- max(slovenia$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Spain"] <- max(spain$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Sweden"] <- max(sweden$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Turkey"] <- max(turkey$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "UAE"] <- max(uae$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "United Kingdom"] <- max(uk$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "United States"] <- max(usa$percentage, na.rm = TRUE) 
x =select(vaccinations1, country, percentage)

y = unique(x) %>% drop_na

yincr = y %>% arrange(y$percentage)
yincr = yincr[!grepl("-Inf", yincr$percentage),]


kable1 =head(yincr,7)
kable1 = kbl(kable1)
kable1
country percentage
Indonesia 0.5908685
India 0.8542487
Oman 0.9525076
Costa Rica 1.1221177
Mexico 1.4100637
Russia 1.5075260
Argentina 1.5365817

Most Vaccinated Country in terms of Population (% of Population)

ydesc = y %>% arrange(y$percentage ,descending=TRUE)

kable2 =tail(ydesc,7)
kable2 = kbl(kable2)
kable2
country percentage
United States 14.63298
Chile 17.28191
Bahrain 17.35427
United Kingdom 28.99279
Seychelles 52.44390
Israel 54.13330
Gibraltar 67.41266

Country Population vaccinated at least once (in Map)

Install necessary packages

library(ggplot2)
install.packages("ggmap")
WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:

https://cran.rstudio.com/bin/windows/Rtools/
trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.6/ggmap_3.0.0.zip'
Content type 'application/zip' length 4698244 bytes (4.5 MB)
downloaded 4.5 MB
package ‘ggmap’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\yoges\AppData\Local\Temp\RtmpqqmemV\downloaded_packages
library(ggmap)
install.packages("maps")
WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:

https://cran.rstudio.com/bin/windows/Rtools/
trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.6/maps_3.3.0.zip'
Content type 'application/zip' length 3695866 bytes (3.5 MB)
downloaded 3.5 MB
package ‘maps’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\yoges\AppData\Local\Temp\RtmpqqmemV\downloaded_packages
library(maps)
install.packages("mapdata")
WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:

https://cran.rstudio.com/bin/windows/Rtools/
trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.6/mapdata_2.3.0.zip'
Content type 'application/zip' length 25640320 bytes (24.5 MB)
downloaded 24.5 MB
package ‘mapdata’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\yoges\AppData\Local\Temp\RtmpqqmemV\downloaded_packages
library(mapdata)
world <- map_data("world")

world$percentage = NA
world$percentage[world$region == "Argentina"] <- max(argentina$percentage, na.rm = TRUE) 
world$percentage[world$region == "Austria"] <- max(austria$percentage, na.rm = TRUE) 
world$percentage[world$region == "Bahrain"] <- max(bahrain$percentage, na.rm = TRUE) 
world$percentage[world$region == "Belgium"] <- max(belgium$percentage, na.rm = TRUE) 
world$percentage[world$region == "Brazil"] <- max(brazil$percentage, na.rm = TRUE)
world$percentage[world$region == "Bulgaria"] <- max(bulgaria$percentage, na.rm = TRUE) 
world$percentage[world$region == "Canada"] <- max(canada$percentage, na.rm = TRUE) 
world$percentage[world$region == "Chile"] <- max(chile$percentage, na.rm = TRUE)
world$percentage[world$region == "China"] <- max(china$percentage, na.rm = TRUE)
world$percentage[world$region == "Costa Rica"] <- max(costa_rica$percentage, na.rm = TRUE)
world$percentage[world$region == "Croatia"] <- max(croatia$percentage, na.rm = TRUE)
world$percentage[world$region == "Cyprus"] <- max(cyprus$percentage, na.rm = TRUE)
world$percentage[world$region == "Denmark"] <- max(denmark$percentage, na.rm = TRUE)
world$percentage[world$region == "Estonia"] <- max(estonia$percentage, na.rm = TRUE)
world$percentage[world$region == "Finland"] <- max(finland$percentage, na.rm = TRUE)
world$percentage[world$region == "France"] <- max(france$percentage, na.rm = TRUE)
world$percentage[world$region == "Germany"] <- max(germany$percentage, na.rm = TRUE)
world$percentage[world$region == "Gibraltar"] <- max(gibraltar$percentage, na.rm = TRUE)
world$percentage[world$region == "Greece"] <- max(greece$percentage, na.rm = TRUE)
world$percentage[world$region == "Hungary"] <- max(hungary$percentage, na.rm = TRUE)
world$percentage[world$region == "Iceland"] <- max(iceland$percentage, na.rm = TRUE)
world$percentage[world$region == "India"] <- max(india$percentage, na.rm = TRUE)
world$percentage[world$region == "Indonesia"] <- max(indonesia$percentage, na.rm = TRUE)
world$percentage[world$region == "Ireland"] <- max(ireland$percentage, na.rm = TRUE)
world$percentage[world$region == "Israel"] <- max(israel$percentage, na.rm = TRUE)
world$percentage[world$region == "Italy"] <- max(italy$percentage, na.rm = TRUE)
world$percentage[world$region == "Kuwait"] <- max(kuwait$percentage, na.rm = TRUE)
world$percentage[world$region == "Latvia"] <- max(latvia$percentage, na.rm = TRUE)
world$percentage[world$region == "Lithuania"] <- max(lithuania$percentage, na.rm = TRUE)
world$percentage[world$region == "Luxembourg"] <- max(luxembourg$percentage, na.rm = TRUE)
world$percentage[world$region == "Malta"] <- max(malta$percentage, na.rm = TRUE)
world$percentage[world$region == "Mexico"] <- max(mexico$percentage, na.rm = TRUE)
world$percentage[world$region == "Norway"] <- max(norway$percentage, na.rm = TRUE)
world$percentage[world$region == "Oman"] <- max(oman$percentage, na.rm = TRUE)
world$percentage[world$region == "Panama"] <- max(panama$percentage, na.rm = TRUE)
world$percentage[world$region == "Poland"] <- max(poland$percentage, na.rm = TRUE)
world$percentage[world$region == "Portugal"] <- max(portugal$percentage, na.rm = TRUE)
world$percentage[world$region == "Romania"] <- max(romania$percentage, na.rm = TRUE)
world$percentage[world$region == "Russia"] <- max(russia$percentage, na.rm = TRUE)
world$percentage[world$region == "Saudi Arabia"] <- max(saudi_arabia$percentage, na.rm = TRUE)
world$percentage[world$region == "Serbia"] <- max(serbia$percentage, na.rm = TRUE)
world$percentage[world$region == "Seychelles"] <- max(seychelles$percentage, na.rm = TRUE)
world$percentage[world$region == "Slovakia"] <- max(slovakia$percentage, na.rm = TRUE)
world$percentage[world$region == "Slovenia"] <- max(slovenia$percentage, na.rm = TRUE)
world$percentage[world$region == "Spain"] <- max(spain$percentage, na.rm = TRUE)
world$percentage[world$region == "Sweden"] <- max(sweden$percentage, na.rm = TRUE)
world$percentage[world$region == "United Arab Emirates"] <- max(uae$percentage, na.rm = TRUE)
world$percentage[world$region == "USA"] <- max(usa$percentage, na.rm = TRUE)
world$percentage[world$region == "UK"] <- max(uk$percentage, na.rm = TRUE)

ca_base = ggplot(data = world, mapping = aes(x = long, y = lat , group = group)) + 
  coord_fixed(1.3)
elbow_room1 = ca_base + 
      geom_polygon(data = world, aes(fill = percentage), color = "black") +
      geom_polygon(color = "black", fill = NA) +
      theme_bw()

elbow_room1 + scale_fill_gradient(low = "light blue", high = "dark blue", na.value = NA)

---
title: "R Notebook"
output: html_notebook
---

# Install necessary packages
```{r, warning = FALSE, message = FALSE}
install.packages("summarytools")
library(DT)
library(readr)
library(ggplot2)
library(knitr)
library(hms)
library(kableExtra)
library(dplyr)
library(magrittr)
library(gridExtra)

library(plotly)
library(Hmisc)
library(ggthemes)
library(tidyr)
library(sysfonts)
font_add_google(name = "Amatic SC", family = "amatic-sc")

library(sf)
library(rnaturalearth)
library(rnaturalearthdata)
library(summarytools)
```
## Import datasets
```{r, message=FALSE}
vaccinations = read_csv("country_vaccinations.csv")
world_population = read_csv("world_population_by_countries.csv")
```
## Dataset Preview
```{r, message=FALSE}
datatable(vaccinations, options = list(
  searching = FALSE,
  pageLength = 10,
  lengthMenu = c(10, 10, 15, 20)
))
```
```{r, message=FALSE}
datatable(world_population, options = list(
  searching = FALSE,
  pageLength = 10,
  lengthMenu = c(10, 10, 15, 20)
))
```
## Descriptive Stats
```{r}
describe(vaccinations, descript = "Life Expectancy") %>% html()
`````
## Countrywise Plots

Plot -- Vaccines per day | Percentage of vaccined population | Daily Vaccinations

### Argentina - AR
```{r, warning = FALSE}
argentina <- vaccinations[grep("ARG", vaccinations$iso_code),]
argentina_world_pop <- world_population[grep("Argentina", world_population$Country),]


argentina$population = argentina_world_pop$Population
argentina$percentage = (argentina$people_vaccinated/argentina$population)*100

plot_argentina <- argentina %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇦🇷") +
    scale_y_continuous(breaks = seq(0, 100000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_argentina_1 = plot_argentina
plot_argentina_1 = plot_argentina_1 + ggtitle("Population Vaccinated on \n Argentina (2021)") + 
    theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))

plot_argentina_percentage <- argentina %>%
  ggplot( aes(x=date, y=percentage, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇦🇷") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_argentina_2 = plot_argentina_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_argentina_2 = plot_argentina_2 + ggtitle("Population (%) Vaccinated \n on Argentina") + 
    theme(plot.title = element_text(size = 7, face = "bold"))


plot_argentina_daily_vac <- argentina %>%
  ggplot( aes(x=date, y=daily_vaccinations)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇦🇷") +
    scale_y_continuous(breaks = seq(0, 100000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    

plot_argentina_interactive <- plot_ly(x = argentina$date, y = argentina$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")
plot_argentina_interactive2 <- plot_ly(x = argentina$date, y = argentina$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_argentina_interactive3 <- plot_ly(x = argentina$date, y = argentina$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")


plotly::subplot(plot_argentina_interactive,plot_argentina_interactive2,plot_argentina_interactive3, nrows=1 , margin= 0.05)
```

### Austria - AT
```{r, warning = FALSE}
austria <- vaccinations[grep("AUT", vaccinations$iso_code),]
austria_world_pop <- world_population[grep("Austria", world_population$Country),]

austria$population = austria_world_pop$Population
austria$percentage = (austria$people_vaccinated/austria$population)*100

plot_austria <- austria %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇦🇹") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_austria_1 = plot_austria+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_austria_1 = plot_austria_1 + ggtitle("Population Vaccinated on \n Austria (2021)")

plot_austria_percentage <- austria %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇦🇹") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_austria_2 = plot_austria_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_austria_2 = plot_austria_2 + ggtitle("Population (%) Vaccinated \n on Austria") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_austria_interactive <- plot_ly(x = austria$date, y = austria$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")
plot_austria_interactive2 <- plot_ly(x = austria$date, y = austria$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_austria_interactive3 <- plot_ly(x = austria$date, y = austria$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_austria_interactive,plot_austria_interactive2,plot_austria_interactive3, nrows=1 , margin= 0.05)
```

### Bahrain - BH
```{r, warning = FALSE}
bahrain <- vaccinations[grep("BHR", vaccinations$iso_code),]
bahrain_world_pop <- world_population[grep("Bahrain", world_population$Country),]

bahrain$population = bahrain_world_pop$Population
bahrain$percentage = (bahrain$people_vaccinated/bahrain$population)*100


bahrain$percentage[is.na(bahrain$percentage)] <- round(mean(bahrain$percentage, na.rm = TRUE))

plot_bahrain <- bahrain %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇧🇭") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_bahrain_1 = plot_bahrain+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_bahrain_1 = plot_bahrain_1 + ggtitle("Population (%) \n Vaccinated on \n Bahrain (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_bahrain_percentage <- bahrain %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇧🇭") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_bahrain_2 = plot_bahrain_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_bahrain_2 = plot_bahrain_2 + ggtitle("Population Vaccinated \n on Bahrain") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_bahrain_interactive <- plot_ly(x = bahrain$date, y = bahrain$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")
plot_bahrain_interactive2 <- plot_ly(x = bahrain$date, y = bahrain$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_bahrain_interactive3 <- plot_ly(x = bahrain$date, y = bahrain$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")


plotly::subplot(plot_bahrain_interactive,plot_bahrain_interactive2,plot_bahrain_interactive3, nrows=1 , margin = 0.05)
```

### Belgium - BE
```{r, warning = FALSE}
belgium <- vaccinations[grep("BEL", vaccinations$iso_code),]
belgium_world_pop <- world_population[grep("Belgium", world_population$Country),]

belgium$population = belgium_world_pop$Population
belgium$percentage = (belgium$people_vaccinated/belgium$population)*100
plot_belgium <- belgium %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇧🇪") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_belgium_1 = plot_belgium+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_belgium_1 = plot_belgium_1 + ggtitle("Population (%) \n Vaccinated on \n Belgium (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_belgium_percentage <- belgium %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇧🇪") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_belgium_2 = plot_belgium_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_belgium_2 = plot_belgium_2 + ggtitle("Population (%) Vaccinated \n on Belgium") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_belgium_interactive <- plot_ly(x = belgium$date, y = belgium$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_belgium_interactive2 <- plot_ly(x = belgium$date, y = belgium$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_belgium_interactive3 <- plot_ly(x = belgium$date, y = belgium$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_belgium_interactive,plot_belgium_interactive2,plot_belgium_interactive3, nrows=1 , margin = 0.05)
```

### Brazil - BR
```{r, warning = FALSE}
brazil <- vaccinations[grep("BRA", vaccinations$iso_code),]
brazil_world_pop <- world_population[grep("Brazil", world_population$Country),]

brazil$population = brazil_world_pop$Population
brazil$percentage = (brazil$people_vaccinated/brazil$population)*100
plot_brazil <- brazil %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇧🇷") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_brazil_1 = plot_brazil+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_brazil_1 = plot_brazil_1 + ggtitle("Population (%) \n Vaccinated on \n Brazil (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_brazil_percentage <- brazil %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇧🇷") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_brazil_2 = plot_brazil_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_brazil_2 = plot_brazil_2 + ggtitle("Population (%) Vaccinated \n on Brazil") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_brazil_interactive <- plot_ly(x = brazil$date, y = brazil$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_brazil_interactive2 <- plot_ly(x = brazil$date, y = brazil$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_brazil_interactive3 <- plot_ly(x = brazil$date, y = brazil$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_brazil_interactive,plot_brazil_interactive2,plot_brazil_interactive3, nrows=1 , margin = 0.05)
```

### Bulgaria - BG
```{r, warning = FALSE}
bulgaria <- vaccinations[grep("BGR", vaccinations$iso_code),]
bulgaria_world_pop <- world_population[grep("Bulgaria", world_population$Country),]

bulgaria$population = bulgaria_world_pop$Population
bulgaria$percentage = (bulgaria$people_vaccinated/bulgaria$population)*100
plot_bulgaria <- bulgaria %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇧🇬") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_bulgaria_1 = plot_bulgaria+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_bulgaria_1 = plot_bulgaria_1 + ggtitle("Population Vaccinated on  \n Bulgaria (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_bulgaria_percentage <- bulgaria %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇧🇬") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_bulgaria_2 = plot_bulgaria_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_bulgaria_2 = plot_bulgaria_2 + ggtitle("Population (%) Vaccinated \n on Bulgaria") + 
    theme(plot.title = element_text(size = 7, face = "bold"))
    
plot_bulgaria_interactive <- plot_ly(x = bulgaria$date, y = bulgaria$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_bulgaria_interactive2 <- plot_ly(x = bulgaria$date, y = bulgaria$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_bulgaria_interactive3 <- plot_ly(x = bulgaria$date, y = bulgaria$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_bulgaria_interactive,plot_bulgaria_interactive2,plot_bulgaria_interactive3, nrows=1 , margin=0.05)
```

### Canada - CA
```{r, warning = FALSE}
canada <- vaccinations[grep("CAN", vaccinations$iso_code),]
canada_world_pop <- world_population[grep("Canada", world_population$Country),]

canada$population = canada_world_pop$Population
canada$percentage = (canada$people_vaccinated/canada$population)*100
plot_canada <- canada %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇦") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_canada_1 = plot_canada+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_canada_1 = plot_canada_1 + ggtitle("Population (%) Vaccinated on Canada (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_canada_percentage <- canada %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇦") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_canada_2 = plot_canada_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_canada_1 = plot_canada_1 + ggtitle("Population Vaccinated \n on Canada") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_canada_interactive <- plot_ly(x = canada$date, y = canada$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_canada_interactive2 <- plot_ly(x = canada$date, y = canada$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_canada_interactive3 <- plot_ly(x = canada$date, y = canada$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_canada_interactive,plot_canada_interactive2,plot_canada_interactive3 ,nrows=1 , margin = 0.05)
```

### Chile - CL
```{r, warning = FALSE}
chile <- vaccinations[grep("CHL", vaccinations$iso_code),]
chile_world_pop <- world_population[grep("Chile", world_population$Country),]

chile$population = chile_world_pop$Population
chile$percentage = (chile$people_vaccinated/chile$population)*100
plot_chile<- chile %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇱") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_chile_1 = plot_chile+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_chile_1 = plot_chile_1 + ggtitle("Population Vaccinated on Chile (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_chile_percentage <- chile %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇱") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_chile_2 = plot_chile_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_chile_2 = plot_chile_2 + ggtitle("Population Vaccinated(%) \n  on Chile") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_chile_interactive <- plot_ly(x = chile$date, y = chile$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_chile_interactive2 <- plot_ly(x = chile$date, y = chile$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_chile_interactive3 <- plot_ly(x = chile$date, y = chile$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_chile_interactive,plot_chile_interactive2,plot_chile_interactive3, nrows=1 , margin = 0.05)
```

### China - CN
```{r, warning = FALSE}
china <- vaccinations[grep("CHN", vaccinations$iso_code),]
china_world_pop <- world_population[grep("China", world_population$Country),]

china$population = china_world_pop$Population
china$percentage = (china$people_vaccinated/china$population)*100
plot_china <- china %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇳") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_china_1 = plot_china+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_china_1 = plot_china_1 + ggtitle("Population Vaccinated on China (2020/21)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_china_percentage <- china %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇳") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_china_2 = plot_china_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_china_2 = plot_china_2 + ggtitle("Population (%) Vaccinated \n on China") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_china_interactive <- plot_ly(x = china$date, y = china$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_china_interactive2 <- plot_ly(x = china$date, y = china$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_china_interactive3 <- plot_ly(x = china$date, y = china$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_china_interactive,plot_china_interactive2,plot_china_interactive3, nrows=1 , margin = 0.05)
````

### Costa Rica - CR
```{r, warning = FALSE}
costa_rica <- vaccinations[grep("CRI", vaccinations$iso_code),]
costa_rica_world_pop <- world_population[grep("Costa Rica", world_population$Country),]

costa_rica$population = costa_rica_world_pop$Population
costa_rica$percentage = (costa_rica$people_vaccinated/costa_rica$population)*100



plot_costa_rica <- costa_rica %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇷") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_costa_rica_1 = plot_costa_rica+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_costa_rica_1 = plot_costa_rica_1 + ggtitle("Population (%) Vaccinated \n on Costa Rica") + 
    theme(plot.title = element_text(size = 6, face = "bold"))

plot_costa_rica_percentage <- costa_rica %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇷") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_costa_rica_2 = plot_costa_rica_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_costa_rica_2 = plot_costa_rica_2 + ggtitle("Population (%) Vaccinated \non  Costa Rica") + 
    theme(plot.title = element_text(size = 6, face = "bold"))

plot_costa_rica_interactive <- plot_ly(x = costa_rica$date, y = costa_rica$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_costa_rica_interactive2 <- plot_ly(x = costa_rica$date, y = costa_rica$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_costa_rica_interactive3 <- plot_ly(x = costa_rica$date, y = costa_rica$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_costa_rica_interactive,plot_costa_rica_interactive2,plot_costa_rica_interactive3, nrows=1 ,margin = 0.05)
```

### Croatia - HR
```{r, warning = FALSE}
croatia <- vaccinations[grep("HRV", vaccinations$iso_code),]
croatia_world_pop <- world_population[grep("Croatia", world_population$Country),]

croatia$population = croatia_world_pop$Population
croatia$percentage = (croatia$people_vaccinated/croatia$population)*100
plot_croatia <- croatia %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇭🇷") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_croatia_1 = plot_croatia+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_croatia_1 = plot_croatia_1 + ggtitle("Population Vaccinated \n on Croatia (2021)") + 
    theme(plot.title = element_text(size = 6, face = "bold"))

plot_croatia_percentage <- croatia %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇭🇷") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_croatia_2 = plot_croatia_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_croatia_2 = plot_croatia_2 + ggtitle("Population (%) Vaccinated on Croatia") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_croatia_interactive <- plot_ly(x = croatia$date, y = croatia$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_croatia_interactive2 <- plot_ly(x = croatia$date, y = croatia$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_croatia_interactive3 <- plot_ly(x = croatia$date, y = croatia$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_croatia_interactive,plot_croatia_interactive2,plot_croatia_interactive3, nrows=1)
```

### Cyprus - CY
```{r, warning = FALSE}
cyprus <- vaccinations[grep("CYP", vaccinations$iso_code),]
cyprus_world_pop <- world_population[grep("Cyprus", world_population$Country),]

cyprus$population = cyprus_world_pop$Population
cyprus$percentage = (cyprus$people_vaccinated/cyprus$population)*100
plot_cyprus <- cyprus %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇾") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_cyprus_1 = plot_cyprus+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_cyprus_1 = plot_cyprus_1 + ggtitle("Population Vaccinated \n on Cyprus (2021)") + 
    theme(plot.title = element_text(size = 6, face = "bold"))

plot_cyprus_percentage <- cyprus %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇾") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_cyprus_2 = plot_cyprus_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_cyprus_2 = plot_cyprus_2 + ggtitle("Population (%) Vaccinated on Cyprus") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_cyprus_interactive <- plot_ly(x = cyprus$date, y = cyprus$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_cyprus_interactive2 <- plot_ly(x = cyprus$date, y = cyprus$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_cyprus_interactive3 <- plot_ly(x = cyprus$date, y = cyprus$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_cyprus_interactive,plot_cyprus_interactive2,plot_cyprus_interactive3, nrows=1)
```

### Czech Republic - CZ
```{r, warning = FALSE}
czech_rep <- vaccinations[grep("CZE", vaccinations$iso_code),]
czech_rep_world_pop <- world_population[grep("Czech", world_population$Country),]

czech_rep$population = czech_rep_world_pop$Population
czech_rep$percentage = (czech_rep$people_vaccinated/czech_rep$population)*100

plot_czech_rep <- czech_rep %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇿") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_czech_rep_1 = plot_czech_rep+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_czech_rep_1 = plot_czech_rep_1 + ggtitle("Population Vaccinated \n on Czech Rep. (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_czech_rep_percentage <- czech_rep %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇨🇿") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_czech_rep_2 = plot_czech_rep_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_czech_rep_2 = plot_czech_rep_2 + ggtitle("Population (%) Vaccinated \n on Czech Rep.") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_czech_rep_interactive <- plot_ly(x = czech_rep$date, y = czech_rep$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_czech_rep_interactive2 <- plot_ly(x = czech_rep$date, y = czech_rep$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_czech_rep_interactive3 <- plot_ly(x = czech_rep$date, y = czech_rep$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_czech_rep_interactive,plot_czech_rep_interactive2,plot_czech_rep_interactive3, nrows=1)
```

### Denmark - DK
```{r, warning = FALSE}
denmark <- vaccinations[grep("DNK", vaccinations$iso_code),]
denmark_world_pop <- world_population[grep("Denmark", world_population$Country),]

denmark$population = denmark_world_pop$Population
denmark$percentage = (denmark$people_vaccinated/denmark$population)*100
plot_denmark <- denmark %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇩🇰") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()
    
plot_denmark_1 = plot_denmark+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_denmark_1 = plot_denmark_1 + ggtitle("Population Vaccinated on Denmark (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_denmark_percentage <- denmark %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇩🇰") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_denmark_2 = plot_denmark_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_denmark_2 = plot_denmark_2 + ggtitle("Population (%) Vaccinated \n on Denmark") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_denmark_interactive <- plot_ly(x = denmark$date, y = denmark$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_denmark_interactive2 <- plot_ly(x = denmark$date, y = denmark$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_denmark_interactive3 <- plot_ly(x = denmark$date, y = denmark$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_denmark_interactive,plot_denmark_interactive2,plot_denmark_interactive3, nrows=1)
```

### Estonia - EE
```{r, warning = FALSE}
estonia <- vaccinations[grep("EST", vaccinations$iso_code),]
estonia_world_pop <- world_population[grep("Estonia", world_population$Country),]

estonia$population = estonia_world_pop$Population
estonia$percentage = (estonia$people_vaccinated/estonia$population)*100
plot_estonia <- estonia %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇪🇪") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_estonia_1 = plot_estonia+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_estonia_1 = plot_estonia_1 + ggtitle("Population Vaccinated \n on Estonia (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_estonia_percentage <- estonia %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇪🇪") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_estonia_2 = plot_estonia_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_estonia_2 = plot_estonia_2 + ggtitle("Population (%) Vaccinated \n on Estonia") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_estonia_interactive <- plot_ly(x = estonia$date, y = estonia$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_estonia_interactive2 <- plot_ly(x = estonia$date, y = estonia$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_estonia_interactive3 <- plot_ly(x = estonia$date, y = estonia$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_estonia_interactive,plot_estonia_interactive2,plot_estonia_interactive3, nrows=1)
```

### Finland - FI
```{r, warning = FALSE}
finland <- vaccinations[grep("FIN", vaccinations$iso_code),]
finland_world_pop <- world_population[grep("Finland", world_population$Country),]

finland$population = finland_world_pop$Population
finland$percentage = (finland$people_vaccinated/finland$population)*100
plot_finland <- finland %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇫🇮") +
    scale_y_continuous(breaks = seq(0, 100, 20),limits = c(0, 100))+
    theme_wsj()
    
plot_finland_1 = plot_finland+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_finland_1 = plot_finland_1 + ggtitle("Population (%) Vaccinated \n on Finland (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_finland_percentage <- finland %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇫🇮") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_finland_2 = plot_finland_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_finland_2 = plot_finland_2 + ggtitle("Population (%) Vaccinated\n on Finland") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_finland_interactive <- plot_ly(x = finland$date, y = finland$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_finland_interactive2 <- plot_ly(x = finland$date, y = finland$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_finland_interactive3 <- plot_ly(x = finland$date, y = finland$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_finland_interactive,plot_finland_interactive2,plot_finland_interactive3, nrows=1)
```

### France - FR
```{r, warning = FALSE}
france <- vaccinations[grep("FRA", vaccinations$iso_code),]
france_world_pop <- world_population[grep("France", world_population$Country),]

france$population = france_world_pop$Population
france$percentage = (france$people_vaccinated/france$population)*100
plot_france <- france %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇫🇷") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_france_1 = plot_france+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_france_1 = plot_france_1 + ggtitle("Population Vaccinated \n on France (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_france_percentage <- france %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇫🇷") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_france_2 = plot_france_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_france_2 = plot_france_2 + ggtitle("Population (%) Vaccinated \n on France") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_france_interactive <- plot_ly(x = france$date, y = france$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_france_interactive2 <- plot_ly(x = france$date, y = france$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_france_interactive3 <- plot_ly(x = france$date, y = france$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_france_interactive,plot_france_interactive2,plot_france_interactive3, nrows=1)
```

### Germany - DE
```{r, warning = FALSE}
germany <- vaccinations[grep("DEU", vaccinations$iso_code),]
germany_world_pop <- world_population[grep("Germany", world_population$Country),]

germany$population = germany_world_pop$Population
germany$percentage = (germany$people_vaccinated/germany$population)*100
plot_germany <- germany %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇩🇪") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_germany_1 = plot_germany+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_germany_1 = plot_germany_1 + ggtitle("Population Vaccinated \n on Germany (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_germany_percentage <- germany %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇩🇪") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_germany_2 = plot_germany_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_germany_2 = plot_germany_2 + ggtitle("Population (%) Vaccinated \n on Germany") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_germany_interactive <- plot_ly(x = germany$date, y = germany$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_germany_interactive2 <- plot_ly(x = germany$date, y = germany$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_germany_interactive3 <- plot_ly(x = germany$date, y = germany$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_germany_interactive,plot_germany_interactive2,plot_germany_interactive3, nrows=1)
```

### Gibraltar - GI
```{r, warning = FALSE}
gibraltar <- vaccinations[grep("GIB", vaccinations$iso_code),]
gibraltar_world_pop <- world_population[grep("Gibraltar", world_population$Country),]

gibraltar$population = gibraltar_world_pop$Population
gibraltar$percentage = (gibraltar$people_vaccinated/gibraltar$population)*100

plot_gibraltar <- gibraltar %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇬🇮") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_gibraltar_1 = plot_gibraltar+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_gibraltar_1 = plot_gibraltar_1 + ggtitle("Population Vaccinated on Gibraltar (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_gibraltar_percentage <- gibraltar %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇬🇮") +
    scale_y_continuous(breaks = seq(0, 40, 10),limits = c(0, 40))+
    theme_wsj()

plot_gibraltar_2 = plot_gibraltar_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_gibraltar_2 = plot_gibraltar_2 + ggtitle("Population (%) Vaccinated \n on Gibraltar") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_gibraltar_interactive <- plot_ly(x = gibraltar$date, y = gibraltar$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_gibraltar_interactive2 <- plot_ly(x = gibraltar$date, y = gibraltar$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_gibraltar_interactive3 <- plot_ly(x = gibraltar$date, y = gibraltar$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_gibraltar_interactive,plot_gibraltar_interactive2,plot_gibraltar_interactive3, nrows=1)
```

### Greece - GR
```{r, warning = FALSE}
greece <- vaccinations[grep("GRC", vaccinations$iso_code),]
greece_world_pop <- world_population[grep("Greece", world_population$Country),]

greece$population = greece_world_pop$Population
greece$percentage = (greece$people_vaccinated/greece$population)*100

plot_greece <- greece %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇬🇷") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_greece_1 = plot_greece+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_greece_1 = plot_greece_1 + ggtitle("Population Vaccinated \n on Greece (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_greece_percentage <- greece %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇬🇷") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_greece_2 = plot_greece_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_greece_2 = plot_greece_2 + ggtitle("Population (%) Vaccinated \n on Greece") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_greece_interactive <- plot_ly(x = greece$date, y = greece$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_greece_interactive2 <- plot_ly(x = greece$date, y = greece$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_greece_interactive3 <- plot_ly(x = greece$date, y = greece$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_greece_interactive,plot_greece_interactive2,plot_greece_interactive3, nrows=1)
```

### Hungary - HU
```{r, warning = FALSE}
hungary <- vaccinations[grep("HUN", vaccinations$iso_code),]
hungary_world_pop <- world_population[grep("Hungary", world_population$Country),]

hungary$population = hungary_world_pop$Population
hungary$percentage = (hungary$people_vaccinated/hungary$population)*100

plot_hungary <- hungary %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇭🇺") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_hungary_1 = plot_hungary + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_hungary_1 = plot_hungary_1 + ggtitle("Population (%) Vaccinated \n on Hungary (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_hungary_percentage <- hungary %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇭🇺") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_hungary_2 = plot_hungary_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_hungary_2 = plot_hungary_2 + ggtitle("Population (%) Vaccinated \n on Hungary") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_hungary_interactive <- plot_ly(x = hungary$date, y = hungary$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_hungary_interactive2 <- plot_ly(x = hungary$date, y = hungary$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_hungary_interactive3 <- plot_ly(x = hungary$date, y = hungary$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_hungary_interactive,plot_hungary_interactive2, plot_hungary_interactive3,nrows=1)
```

### Iceland - IS
```{r, warning = FALSE}
iceland <- vaccinations[grep("ISL", vaccinations$iso_code),]
iceland_world_pop <- world_population[grep("Iceland", world_population$Country),]

iceland$population = iceland_world_pop$Population
iceland$percentage = (iceland$people_vaccinated/iceland$population)*100

plot_iceland <- iceland %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇸") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_iceland_1 = plot_iceland + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_iceland_1 = plot_iceland_1 + ggtitle("Population Vaccinated on Iceland (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_iceland_percentage <- iceland %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇸") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_iceland_2 = plot_iceland_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_iceland_2 = plot_iceland_2 + ggtitle("Population (%) Vaccinated \n on Iceland") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_iceland_interactive <- plot_ly(x = iceland$date, y = iceland$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_iceland_interactive2 <- plot_ly(x = iceland$date, y = iceland$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_iceland_interactive3 <- plot_ly(x = iceland$date, y = iceland$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_iceland_interactive,plot_iceland_interactive2,plot_iceland_interactive3 ,nrows=1)
```

### India - IN
```{r, warning = FALSE}
india <- vaccinations[grep("IND", vaccinations$iso_code),]
india_world_pop <- world_population[grep("India", world_population$Country),]

india$population = india_world_pop$Population
india$percentage = (india$people_vaccinated/india$population)*100


plot_india <- india %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇳") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_india_1 = plot_india + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_india_1 = plot_india_1 + ggtitle("Population Vaccinated on India (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_india_percentage <- india %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇳") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_india_2 = plot_india_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_india_2 = plot_india_2 + ggtitle("Population (%) Vaccinated \n on India (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_india_interactive <- plot_ly(x = india$date, y = india$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_india_interactive2 <- plot_ly(x = india$date, y = india$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_india_interactive3 <- plot_ly(x = india$date, y = india$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_india_interactive,plot_india_interactive2,plot_india_interactive3, nrows=1)
```

### Indonesia - ID
```{r, warning = FALSE}
indonesia <- vaccinations[grep("IDN", vaccinations$iso_code),]
indonesia_world_pop <- world_population[grep("Indonesia", world_population$Country),]

indonesia$population = indonesia_world_pop$Population
indonesia$percentage = (indonesia$people_vaccinated/indonesia$population)*100


plot_indonesia <- indonesia %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇩") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_indonesia_1 = plot_indonesia + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_indonesia_1 = plot_indonesia_1 + ggtitle("Population Vaccinated on Indonesia (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_indonesia_percentage <- indonesia %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇩") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_indonesia_2 = plot_indonesia_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_indonesia_2 = plot_indonesia_2 + ggtitle("Population (%) Vaccinated \n on Indonesia (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_indonesia_interactive <- plot_ly(x = indonesia$date, y = indonesia$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_indonesia_interactive2 <- plot_ly(x = indonesia$date, y = indonesia$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_indonesia_interactive3 <- plot_ly(x = indonesia$date, y = indonesia$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_indonesia_interactive,plot_indonesia_interactive2,plot_indonesia_interactive3, nrows=1)

```

### Ireland - IE
```{r, warning = FALSE}
ireland <- vaccinations[grep("IRL", vaccinations$iso_code),]
ireland_world_pop <- world_population[grep("Ireland", world_population$Country),]

ireland$population = ireland_world_pop$Population
ireland$percentage = (ireland$people_vaccinated/ireland$population)*100


plot_ireland <- ireland %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇪") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_ireland_1 = plot_ireland + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_ireland_1 = plot_ireland_1 + ggtitle("Population Vaccinated \n on Ireland (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_ireland_percentage <- ireland %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇪") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_ireland_2 = plot_ireland_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_ireland_2 = plot_ireland_2 + ggtitle("Population (%) Vaccinated \n on Ireland (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_ireland_interactive <- plot_ly(x = ireland$date, y = ireland$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_ireland_interactive2 <- plot_ly(x = ireland$date, y = ireland$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_ireland_interactive3 <- plot_ly(x = ireland$date, y = ireland$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_ireland_interactive,plot_ireland_interactive2,plot_ireland_interactive3, nrows=1)
```

### Israel - IL
```{r, warning = FALSE}
israel <- vaccinations[grep("ISR", vaccinations$iso_code),]
israel_world_pop <- world_population[grep("Israel", world_population$Country),]

israel$population = israel_world_pop$Population
israel$percentage = (israel$people_vaccinated/israel$population)*100


plot_israel <- israel %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇱") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_israel_1 = plot_israel + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_israel_1 = plot_israel_1 + ggtitle("Population Vaccinated \n on Israel (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_israel_percentage <- israel %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇱") +
    scale_y_continuous(breaks = seq(0, 40, 5),limits = c(0, 40))+
    theme_wsj()

plot_israel_2 = plot_israel_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_israel_2 = plot_israel_2 + ggtitle("Population (%) Vaccinated \n on Israel (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_israel_interactive <- plot_ly(x = israel$date, y = israel$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_israel_interactive2 <- plot_ly(x = israel$date, y = israel$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_israel_interactive3 <- plot_ly(x = israel$date, y = israel$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_israel_interactive,plot_israel_interactive2,plot_israel_interactive3, nrows=1)
```

### Italy - IT
```{r, warning = FALSE}
italy <- vaccinations[grep("ITA", vaccinations$iso_code),]
italy_world_pop <- world_population[grep("Italy", world_population$Country),]

italy$population = italy_world_pop$Population
italy$percentage = (italy$people_vaccinated/italy$population)*100


plot_italy <- italy %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇹") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_italy_1 = plot_italy + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_italy_1 = plot_italy_1 + ggtitle("Population Vaccinated \n on Italy(2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_italy_percentage <- italy %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇮🇹") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_italy_2 = plot_italy_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_italy_2 = plot_italy_2 + ggtitle("Population (%) Vaccinated \n on Italy (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_italy_interactive <- plot_ly(x = italy$date, y = italy$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_italy_interactive2 <- plot_ly(x = italy$date, y = italy$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_italy_interactive3 <- plot_ly(x = italy$date, y = italy$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_italy_interactive,plot_italy_interactive2,plot_italy_interactive3, nrows=1)
```

### Kuwait - KW
```{r, warning = FALSE}
kuwait <- vaccinations[grep("KWT", vaccinations$iso_code),]
kuwait_world_pop <- world_population[grep("Kuwait", world_population$Country),]

kuwait$population = kuwait_world_pop$Population
kuwait$percentage = (kuwait$people_vaccinated/kuwait$population)*100


plot_kuwait <- kuwait %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇰🇼") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_kuwait_1 = plot_kuwait + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_kuwait_1 = plot_kuwait_1 + ggtitle("Population Vaccinated \n on Kuwait(2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_kuwait_percentage <- kuwait %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇰🇼") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_kuwait_2 = plot_kuwait_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_kuwait_2 = plot_kuwait_2 + ggtitle("Population (%) Vaccinated \n on Kuwait (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_kuwait_interactive <- plot_ly(x = kuwait$date, y = kuwait$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_kuwait_interactive2 <- plot_ly(x = kuwait$date, y = kuwait$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_kuwait_interactive3 <- plot_ly(x = kuwait$date, y = kuwait$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_kuwait_interactive,plot_kuwait_interactive2,plot_kuwait_interactive3, nrows=1)
```

### Latvia - LV
```{r, warning = FALSE}
latvia <- vaccinations[grep("LVA", vaccinations$iso_code),]
latvia_world_pop <- world_population[grep("Latvia", world_population$Country),]

latvia$population = latvia_world_pop$Population
latvia$percentage = (latvia$people_vaccinated/latvia$population)*100


plot_latvia <- latvia %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇱🇻") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_latvia_1 = plot_latvia + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_latvia_1 = plot_latvia_1 + ggtitle("Population Vaccinated \n on Italy (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_latvia_percentage <- latvia %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇱🇻") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_latvia_2 = plot_latvia_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_latvia_2 = plot_latvia_2 + ggtitle("Population (%) Vaccinated \n on Latvia ") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_latvia_interactive <- plot_ly(x = latvia$date, y = latvia$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_latvia_interactive2 <- plot_ly(x = latvia$date, y = latvia$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_latvia_interactive3 <- plot_ly(x = latvia$date, y = latvia$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_latvia_interactive,plot_latvia_interactive2,plot_latvia_interactive3, nrows=1)
```

### Lithuania - LT
```{r, warning = FALSE}
lithuania <- vaccinations[grep("LTU", vaccinations$iso_code),]
lithuania_world_pop <- world_population[grep("Lithuania", world_population$Country),]

lithuania$population = lithuania_world_pop$Population
lithuania$percentage = (lithuania$people_vaccinated/lithuania$population)*100


plot_lithuania <- lithuania %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇱🇹") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_lithuania_1 = plot_lithuania + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_lithuania_1 = plot_lithuania_1 + ggtitle("Population Vaccinated \n on Lithuania ") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_lithuania_percentage <- lithuania %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇱🇹") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_lithuania_2 = plot_lithuania_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_lithuania_2 = plot_lithuania_2 + ggtitle("Population (%) Vaccinated \n on Lithuania (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_lithuania_interactive <- plot_ly(x = lithuania$date, y = lithuania$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_lithuania_interactive2 <- plot_ly(x = lithuania$date, y = lithuania$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_lithuania_interactive3 <- plot_ly(x = lithuania$date, y = lithuania$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_lithuania_interactive,plot_lithuania_interactive2,plot_lithuania_interactive3, nrows=1)
```

### Luxemberg - LU
```{r, warning = FALSE}
luxembourg <- vaccinations[grep("LUX", vaccinations$iso_code),]
luxembourg_world_pop <- world_population[grep("Luxembourg", world_population$Country),]

luxembourg$population = luxembourg_world_pop$Population
luxembourg$percentage = (luxembourg$people_vaccinated/luxembourg$population)*100


plot_luxembourg <- luxembourg %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇱🇺") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_luxembourg_1 = plot_luxembourg + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_luxembourg_1 = plot_luxembourg_1 + ggtitle("Population Vaccinated \n on Luxembourg") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_luxembourg_percentage <- italy %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇱🇺") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_luxembourg_2 = plot_luxembourg_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_luxembourg_2 = plot_luxembourg_2 + ggtitle("Population (%) Vaccinated \n on Luxembourg") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_luxembourg_interactive <- plot_ly(x = luxembourg$date, y = luxembourg$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_luxembourg_interactive2 <- plot_ly(x = luxembourg$date, y = luxembourg$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_luxembourg_interactive3 <- plot_ly(x = luxembourg$date, y = luxembourg$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_luxembourg_interactive,plot_luxembourg_interactive2,plot_luxembourg_interactive3, nrows=1)
```

### Malta -  MT
```{r, warning = FALSE}
malta <- vaccinations[grep("MLT", vaccinations$iso_code),]
malta_world_pop <- world_population[grep("Malta", world_population$Country),]

malta$population = malta_world_pop$Population
malta$percentage = (malta$people_vaccinated/malta$population)*100


plot_malta <- malta %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇲🇹") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_malta_1 = plot_malta + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_malta_1 = plot_malta_1 + ggtitle("Population Vaccinated \n on Malta (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_malta_percentage <- malta %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇲🇹") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_malta_2 = plot_malta_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_malta_2 = plot_malta_2 + ggtitle("Population (%) Vaccinated \n on Malta") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_malta_interactive <- plot_ly(x = malta$date, y = malta$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_malta_interactive2 <- plot_ly(x = malta$date, y = malta$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_malta_interactive3 <- plot_ly(x = malta$date, y = malta$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_malta_interactive,plot_malta_interactive2,plot_malta_interactive3, nrows=1)
```

### Mexico - MX
```{r, warning = FALSE}
mexico <- vaccinations[grep("MEX", vaccinations$iso_code),]
mexico_world_pop <- world_population[grep("Mexico", world_population$Country),]

mexico$population = mexico_world_pop$Population
mexico$percentage = (mexico$people_vaccinated/mexico$population)*100


plot_mexico <- mexico %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇲🇽") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_mexico_1 = plot_mexico + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_mexico_1 = plot_mexico_1 + ggtitle("Population Vaccinated \n on Mexico ") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_mexico_percentage <- mexico %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇲🇽") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_mexico_2 = plot_mexico_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_mexico_2 = plot_mexico_2 + ggtitle("Population (%) Vaccinated \n on Mexico (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_mexico_interactive <- plot_ly(x = mexico$date, y = mexico$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_mexico_interactive2 <- plot_ly(x = mexico$date, y = mexico$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_mexico_interactive3 <- plot_ly(x = mexico$date, y = mexico$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_mexico_interactive,plot_mexico_interactive2,plot_mexico_interactive3, nrows=1)
```

### Norway - NO
```{r, warning = FALSE}
norway <- vaccinations[grep("NOR", vaccinations$iso_code),]
norway_world_pop <- world_population[grep("Norway", world_population$Country),]

norway$population = norway_world_pop$Population
norway$percentage = (norway$people_vaccinated/norway$population)*100


plot_norway <- norway %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇳🇴") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_norway_1 = plot_norway + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_norway_1 = plot_norway_1 + ggtitle("Population Vaccinated \n on Norway (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_norway_percentage <- norway %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇳🇴") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_norway_2 = plot_norway_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_norway_2 = plot_norway_2 + ggtitle("Population (%) Vaccinated \n on Morway (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_norway_interactive <- plot_ly(x = norway$date, y = norway$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_norway_interactive2 <- plot_ly(x = norway$date, y = norway$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_norway_interactive3 <- plot_ly(x = norway$date, y = norway$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_norway_interactive,plot_norway_interactive2,plot_norway_interactive3, nrows=1)
```

### Oman - OM
```{r, warning = FALSE}
oman <- vaccinations[grep("OMN", vaccinations$iso_code),]
oman_world_pop <- world_population[grep("Oman", world_population$Country),]

oman$population = oman_world_pop$Population
oman$percentage = (oman$people_vaccinated/oman$population)*100


plot_oman <- oman %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇴🇲") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_oman_1 = plot_oman + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_oman_1 = plot_oman_1 + ggtitle("Population Vaccinated \n on Oman(2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_oman_percentage <- oman %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇴🇲") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_oman_2 = plot_oman_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_oman_2 = plot_oman_2 + ggtitle("Population (%) Vaccinated \n on Oman (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_oman_interactive <- plot_ly(x = oman$date, y = oman$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_oman_interactive2 <- plot_ly(x = oman$date, y = oman$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_oman_interactive3 <- plot_ly(x = oman$date, y = oman$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_oman_interactive,plot_oman_interactive2,plot_oman_interactive3, nrows=1)
```

### Panama - PA
```{r, warning = FALSE}
panama <- vaccinations[grep("PAN", vaccinations$iso_code),]
panama_world_pop <- world_population[grep("Panama", world_population$Country),]

panama$population = panama_world_pop$Population
panama$percentage = (panama$people_vaccinated/panama$population)*100


plot_panama <- panama %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇵🇦") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_panama_1 = plot_panama + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_panama_1 = plot_panama_1 + ggtitle("Population  Vaccinated \n on Panama (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_panama_percentage <- panama %>%
  ggplot( aes(x=date, y = percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇵🇦") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_panama_2 = plot_panama_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_panama_2 = plot_panama_2 + ggtitle("Population (%) Vaccinated \n on Panama (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_panama_interactive <- plot_ly(x = panama$date, y = panama$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_panama_interactive2 <- plot_ly(x = panama$date, y = panama$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_panama_interactive3 <- plot_ly(x = panama$date, y = panama$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_panama_interactive,plot_panama_interactive2,plot_panama_interactive3, nrows=1)
```

### Poland - PL
```{r, warning = FALSE}
poland <- vaccinations[grep("POL", vaccinations$iso_code),]
poland_world_pop <- world_population[grep("Poland", world_population$Country),]

poland$population = poland_world_pop$Population
poland$percentage = (poland$people_vaccinated/poland$population)*100


plot_poland <- poland %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇵🇱") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_poland_1 = plot_poland + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_poland_1 = plot_poland_1 + ggtitle("Population  Vaccinated \n on Poland (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_poland_percentage <- poland %>%
  ggplot( aes(x=date, y = percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇵🇱") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_poland_2 = plot_poland_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_poland_2 = plot_poland_2 + ggtitle("Population (%) Vaccinated \n on Poland (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_poland_interactive <- plot_ly(x = poland$date, y = poland$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_poland_interactive2 <- plot_ly(x = poland$date, y = poland$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_poland_interactive3 <- plot_ly(x = poland$date, y = poland$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_poland_interactive,plot_poland_interactive2,plot_poland_interactive3, nrows=1)
```

### Portugal - PT
```{r, warning = FALSE}
portugal <- vaccinations[grep("PRT", vaccinations$iso_code),]
portugal_world_pop <- world_population[grep("Portugal", world_population$Country),]

portugal$population = portugal_world_pop$Population
portugal$percentage = (portugal$people_vaccinated/portugal$population)*100


plot_portugal <- portugal %>%
  ggplot( aes(x=date, y=people_vaccinated, group=1)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇵🇹") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_portugal_1 = plot_portugal + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_portugal_1 = plot_portugal_1 + ggtitle("Population Vaccinated \n on Portugal (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_portugal_percentage <- portugal %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇵🇹") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_portugal_2 = plot_portugal_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_portugal_2 = plot_portugal_2 + ggtitle("Population (%) Vaccinated \n on Portugal (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_portugal_interactive <- plot_ly(x = portugal$date, y = portugal$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_portugal_interactive2 <- plot_ly(x = portugal$date, y = portugal$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_portugal_interactive3 <- plot_ly(x = portugal$date, y = portugal$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_portugal_interactive,plot_portugal_interactive2,plot_portugal_interactive3, nrows=1)
```

### Romania - RO
```{r, warning = FALSE}
romania <- vaccinations[grep("ROU", vaccinations$iso_code),]
romania_world_pop <- world_population[grep("Romania", world_population$Country),]

romania$population = romania_world_pop$Population
romania$percentage = (romania$people_vaccinated/romania$population)*100


plot_romania <- romania %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇷🇴") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_romania_1 = plot_romania + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_romania_1 = plot_romania_1 + ggtitle("Vaccination on \n Romania(2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_romania_percentage <- romania %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇷🇴") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_romania_2 = plot_romania_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_romania_2 = plot_romania_2 + ggtitle("Vaccination on \n Romania") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_romania_interactive <- plot_ly(x = romania$date, y = romania$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_romania_interactive2 <- plot_ly(x = romania$date, y = romania$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_romania_interactive3 <- plot_ly(x = romania$date, y = romania$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_romania_interactive,plot_romania_interactive2,plot_romania_interactive3, nrows=1)
```

### Russia - RU
```{r, warning = FALSE}
russia <- vaccinations[grep("RUS", vaccinations$iso_code),]
russia_world_pop <- world_population[grep("Russia", world_population$Country),]

russia$population = russia_world_pop$Population
russia$percentage = (russia$people_vaccinated/russia$population)*100


plot_russia <- russia %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇷🇺") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_russia_1 = plot_russia + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_russia_1 = plot_russia_1 + ggtitle("Vaccination on \n Russia(2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_russia_percentage <- russia %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇷🇺") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_russia_2 = plot_russia_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_russia_2 = plot_russia_2 + ggtitle("Vaccination on \n Russia") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_russia_interactive <- plot_ly(x = russia$date, y = russia$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_russia_interactive2 <- plot_ly(x = russia$date, y = russia$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_russia_interactive3 <- plot_ly(x = russia$date, y = russia$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_russia_interactive,plot_russia_interactive2,plot_russia_interactive3, nrows=1)
```

### Saudi Arabia - SA
```{r, warning = FALSE}
saudi_arabia <- vaccinations[grep("SAU", vaccinations$iso_code),]
saudi_arabia_world_pop <- world_population[grep("Saudi Arabia", world_population$Country),]

saudi_arabia$population = saudi_arabia_world_pop$Population
saudi_arabia$percentage = (saudi_arabia$people_vaccinated/saudi_arabia$population)*100


plot_saudi_arabia <- saudi_arabia %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇸🇦") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_saudi_arabia_1 = plot_saudi_arabia + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_saudi_arabia_1 = plot_saudi_arabia_1 + ggtitle("Vaccination on \n Saudi Arabia (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_saudi_arabia_percentage <- saudi_arabia %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇸🇦") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_saudi_arabia_2 = plot_saudi_arabia_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_saudi_arabia_2 = plot_saudi_arabia_2 + ggtitle("Vaccination on \n Saudi Arabia (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_saudi_arabia_interactive <- plot_ly(x = saudi_arabia$date, y = saudi_arabia$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_saudi_arabia_interactive2 <- plot_ly(x = saudi_arabia$date, y = saudi_arabia$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_saudi_arabia_interactive3 <- plot_ly(x = saudi_arabia$date, y = saudi_arabia$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_saudi_arabia_interactive,plot_saudi_arabia_interactive2,plot_saudi_arabia_interactive3, nrows=1)
```

### Serbia - RS
```{r, warning = FALSE}
serbia <- vaccinations[grep("SRB", vaccinations$iso_code),]
serbia_world_pop <- world_population[grep("Serbia", world_population$Country),]

serbia$population = serbia_world_pop$Population
serbia$percentage = (serbia$people_vaccinated/serbia$population)*100


plot_serbia <- serbia %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇷🇸") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_serbia_1 = plot_serbia + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_serbia_1 = plot_serbia_1 + ggtitle("Vaccination on \n Serbia (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_serbia_percentage <- serbia %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇷🇸") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_serbia_2 = plot_serbia_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_serbia_2 = plot_serbia_2 + ggtitle("Vaccination on \n Serbia (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_serbia_interactive <- plot_ly(x = serbia$date, y = serbia$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_serbia_interactive2 <- plot_ly(x = serbia$date, y = serbia$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_serbia_interactive3 <- plot_ly(x = serbia$date, y = serbia$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_serbia_interactive,plot_serbia_interactive2,plot_serbia_interactive3, nrows=1)
```

### Seychelles - SC
```{r, warning = FALSE}
seychelles <- vaccinations[grep("SYC", vaccinations$iso_code),]
seychelles_world_pop <- world_population[grep("Seychelles", world_population$Country),]

seychelles$population = seychelles_world_pop$Population
seychelles$percentage = (seychelles$people_vaccinated/seychelles$population)*100


plot_seychelles <- seychelles %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇸🇨") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_seychelles_1 = plot_seychelles + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_seychelles_1 = plot_seychelles_1 + ggtitle("Vaccination on \n Seychelles (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_seychelles_percentage <- seychelles %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇸🇨") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_seychelles_2 = plot_seychelles_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_seychelles_2 = plot_seychelles_2 + ggtitle("Vaccination on \n Seychelles (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_seychelles_interactive <- plot_ly(x = seychelles$date, y = seychelles$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_seychelles_interactive2 <- plot_ly(x = seychelles$date, y = seychelles$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_seychelles_interactive3 <- plot_ly(x = seychelles$date, y = seychelles$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_seychelles_interactive,plot_seychelles_interactive2,plot_seychelles_interactive3, nrows=1)
```

### Slovakia - SK
```{r, warning = FALSE}
slovakia <- vaccinations[grep("SVK", vaccinations$iso_code),]
slovakia_world_pop <- world_population[grep("Slovakia", world_population$Country),]

slovakia$population = slovakia_world_pop$Population
slovakia$percentage = (slovakia$people_vaccinated/slovakia$population)*100


plot_slovakia <- slovakia %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇸🇰") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_slovakia_1 = plot_slovakia + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_slovakia_1 = plot_slovakia_1 + ggtitle("Vaccination on \n Slovakia (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_slovakia_percentage <- slovakia %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇸🇰") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_slovakia_2 = plot_slovakia_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_slovakia_2 = plot_slovakia_2 + ggtitle("Vaccination on \n Slovakia (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_slovakia_interactive <- plot_ly(x = slovakia$date, y = slovakia$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_slovakia_interactive2 <- plot_ly(x = slovakia$date, y = slovakia$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_slovakia_interactive3 <- plot_ly(x = slovakia$date, y = slovakia$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_slovakia_interactive,plot_slovakia_interactive2,plot_slovakia_interactive3 , nrows=1)
```

### Slovenia - SI
```{r, warning = FALSE}
slovenia <- vaccinations[grep("SVN", vaccinations$iso_code),]
slovenia_world_pop <- world_population[grep("Slovenia", world_population$Country),]

slovenia$population = slovenia_world_pop$Population
slovenia$percentage = (slovenia$people_vaccinated/slovenia$population)*100


plot_slovenia <- slovenia %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇸🇮") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_slovenia_1 = plot_slovenia + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_slovenia_1 = plot_slovenia_1 + ggtitle("Vaccination on \n Slovenia (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_slovenia_percentage <- slovenia %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇸🇮") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_slovenia_2 = plot_slovenia_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_slovenia_2 = plot_slovenia_2 + ggtitle("Vaccination on \n Slovenia (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_slovenia_interactive <- plot_ly(x = slovenia$date, y = slovenia$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_slovenia_interactive2 <- plot_ly(x = slovenia$date, y = slovenia$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_slovenia_interactive3 <- plot_ly(x = slovenia$date, y = slovenia$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_slovenia_interactive,plot_slovenia_interactive2,plot_slovenia_interactive3, nrows=1)
```

### Spain - ES
```{r, warning = FALSE}
spain <- vaccinations[grep("ESP", vaccinations$iso_code),]
spain_world_pop <- world_population[grep("Spain", world_population$Country),]

spain$population = spain_world_pop$Population
spain$percentage = (spain$people_vaccinated/spain$population)*100


plot_spain <- spain %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇪🇸") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_spain_1 = plot_spain + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_spain_1 = plot_spain_1 + ggtitle("Vaccination on \n Spain (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_spain_percentage <- spain %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇪🇸") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_spain_2 = plot_spain_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_spain_2 = plot_spain_2 + ggtitle("Vaccination on \n Spain (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_spain_interactive <- plot_ly(x = spain$date, y = spain$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_spain_interactive2 <- plot_ly(x = spain$date, y = spain$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_spain_interactive3 <- plot_ly(x = spain$date, y = spain$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_spain_interactive,plot_spain_interactive2,plot_spain_interactive3, nrows=1)
```

### Sweden - SE
```{r, warning = FALSE}
sweden <- vaccinations[grep("SWE", vaccinations$iso_code),]
sweden_world_pop <- world_population[grep("Sweden", world_population$Country),]

sweden$population = sweden_world_pop$Population
sweden$percentage = (sweden$people_vaccinated/sweden$population)*100


plot_sweden <- sweden %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇸🇪") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_sweden_1 = plot_sweden + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_sweden_1 = plot_sweden_1 + ggtitle("Vaccination on \n Sweden (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_sweden_percentage <- sweden %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇸🇪") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_sweden_2 = plot_sweden_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_sweden_2 = plot_sweden_2 + ggtitle("Vaccination on \n Sweden (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_sweden_interactive <- plot_ly(x = sweden$date, y = sweden$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_sweden_interactive2 <- plot_ly(x = sweden$date, y = sweden$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_sweden_interactive3 <- plot_ly(x = sweden$date, y = sweden$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_sweden_interactive,plot_sweden_interactive2,plot_sweden_interactive3, nrows=1)
```

### Turkey - TR
```{r, warning = FALSE}
turkey <- vaccinations[grep("TUR", vaccinations$iso_code),]
turkey_world_pop <- world_population[grep("Turkey", world_population$Country),]

turkey$population = turkey_world_pop$Population
turkey$percentage = (turkey$people_vaccinated/turkey$population)*100


plot_turkey <- turkey %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇹🇷") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_turkey_1 = plot_turkey + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_turkey_1 = plot_turkey_1 + ggtitle("Vaccination on \n Turkey (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_turkey_percentage <- turkey %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇹🇷") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_turkey_2 = plot_turkey_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_turkey_2 = plot_turkey_2 + ggtitle("Vaccination on \n Turkey (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_turkey_interactive <- plot_ly(x = turkey$date, y = turkey$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_turkey_interactive2 <- plot_ly(x = turkey$date, y = turkey$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_turkey_interactive3 <- plot_ly(x = turkey$date, y = turkey$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_turkey_interactive,plot_turkey_interactive2,plot_turkey_interactive3, nrows=1)
```

### United Arab Emirates - AE
```{r, warning = FALSE}
uae <- vaccinations[grep("ARE", vaccinations$iso_code),]
uae_world_pop <- world_population[grep("United Arab Emirates", world_population$Country),]

uae$population = uae_world_pop$Population
uae$percentage = (uae$people_vaccinated/uae$population)*100


plot_uae <- uae %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇦🇪") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_uae_1 = plot_uae + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_uae_1 = plot_uae_1 + ggtitle("Vaccination on \n UAE (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_uae_percentage <- uae %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇦🇪") +
    scale_y_continuous(breaks = seq(0, 40, 5),limits = c(0, 40))+
    theme_wsj()

plot_uae_2 = plot_uae_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_uae_2 = plot_uae_2 + ggtitle("Vaccination on \n UAE (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_uae_interactive <- plot_ly(x = uae$date, y = uae$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_uae_interactive2 <- plot_ly(x = uae$date, y = uae$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_uae_interactive3 <- plot_ly(x = uae$date, y = uae$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_uae_interactive,plot_uae_interactive2,plot_uae_interactive3, nrows=1)
```

### United Kingdom - UK
```{r, warning = FALSE}
uk <- vaccinations[grep("GBR", vaccinations$iso_code),]
uk_world_pop <- world_population[grep("United Kingdom", world_population$Country),]

uk$population = uk_world_pop$Population
uk$percentage = (uk$people_vaccinated/uk$population)*100


plot_uk <- uk %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇬🇧") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_uk_1 = plot_uk + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_uk_1 = plot_uk_1 + ggtitle("Vaccination on \n United Kingdom (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_uk_percentage <- uk %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇬🇧") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_uk_2 = plot_uk_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_uk_2 = plot_uk_2 + ggtitle("Vaccination on \n UK (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_uk_interactive <- plot_ly(x = uk$date, y = uk$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_uk_interactive2 <- plot_ly(x = uk$date, y = uk$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_uk_interactive3 <- plot_ly(x = uk$date, y = uk$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_uk_interactive,plot_uk_interactive2,plot_uk_interactive3 , nrows=1)
```

### United States of America - US
```{r, warning = FALSE}
usa <- vaccinations[grep("USA", vaccinations$iso_code),]
usa_world_pop <- world_population[grep("United States", world_population$Country),]

usa$population = usa_world_pop$Population
usa$percentage = (usa$people_vaccinated/usa$population)*100


plot_usa <- usa %>%
  ggplot( aes(x=date, y=people_vaccinated)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇺🇸") +
    scale_y_continuous(breaks = seq(0, 10000000, 2000000),limits = c(0, 10000000))+
    theme_wsj()
    
plot_usa_1 = plot_usa + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_usa_1 = plot_usa_1 + ggtitle("Vaccination on \n USA (2021)") + 
    theme(plot.title = element_text(size = 10, face = "bold"))

plot_usa_percentage <- usa %>%
  ggplot( aes(x=date, y=percentage)) +
    geom_point()+
    geom_line(color="#69b3a2") +
    xlab("🇺🇸") +
    scale_y_continuous(breaks = seq(0, 20, 2),limits = c(0, 20))+
    theme_wsj()

plot_usa_2 = plot_usa_percentage+ theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=7, angle=45))
plot_usa_2 = plot_usa_2 + ggtitle("Vaccination on \n USA (2021)") + 
    theme(plot.title = element_text(size = 7, face = "bold"))

plot_usa_interactive <- plot_ly(x = usa$date, y = usa$people_vaccinated, type="scatter", mode="markers", fill = "tozeroy")

plot_usa_interactive2 <- plot_ly(x = usa$date, y = usa$percentage, type="scatter", mode="markers", fill = "tozeroy")
plot_usa_interactive3 <- plot_ly(x = usa$date, y = usa$daily_vaccinations, type="scatter", mode="markers", fill = "tozeroy")

plotly::subplot(plot_usa_interactive,plot_usa_interactive2,plot_usa_interactive3, nrows=1)
```

## Summarizing Plots

### Percentage of population vaccinated
```{r, warning = FALSE}
x1 = grid.arrange(plot_argentina_2,plot_austria_2,plot_bahrain_2,plot_belgium_2,plot_bulgaria_2, plot_chile_2,nrow=2)
```

```{r, warning = FALSE}
x2 = grid.arrange(plot_china_2 , plot_costa_rica_2, plot_croatia_2, plot_czech_rep_2, plot_denmark_2, plot_estonia_2,nrow=2)
```

```{r, warning = FALSE}
x3 = grid.arrange(plot_finland_2 , plot_france_2 , plot_germany_2,plot_gibraltar_2 , plot_greece_2, plot_hungary_2 , nrow = 2)
```

```{r, warning = FALSE}
x4 = grid.arrange(plot_iceland_2 , plot_india_2 , plot_ireland_2,plot_israel_2 , plot_italy_2, plot_latvia_2 , nrow = 2)
```

```{r, warning = FALSE}
x5 = grid.arrange(plot_lithuania_2 , plot_luxembourg_2 , plot_malta_2,plot_mexico_2 , plot_norway_2, plot_oman_2, nrow = 2)
```

```{r, warning = FALSE}
x6 = grid.arrange(plot_poland_2 , plot_portugal_2 , plot_romania_2,plot_saudi_arabia_2, plot_serbia_2, plot_seychelles_1 , nrow = 2)
```

```{r, warning = FALSE}
x7 = grid.arrange(plot_slovakia_2 , plot_slovenia_2 , plot_spain_2,plot_sweden_2 , plot_turkey_2, plot_uae_2 , plot_uk_2 , plot_usa_2, nrow = 2)
```

## Less Vaccinated Country in terms of Population (% of Population)
```{r, warning = FALSE}
vaccinations1 = vaccinations
vaccinations1$percentage[vaccinations$country == "Argentina"] <- max(argentina$percentage , na.rm = TRUE)
vaccinations1$percentage[vaccinations$country == "Austria"] <- max(austria$percentage , na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Bahrain"] <- max(bahrain$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Belgium"] <- max(belgium$percentage, na.rm = TRUE)
vaccinations1$percentage[vaccinations$country == "Brazil"] <- max(brazil$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Bulgaria"] <- max(bulgaria$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Canada"] <- max(canada$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Chile"] <- max(chile$percentage, na.rm = TRUE)
vaccinations1$percentage[vaccinations$country == "China"] <- max(china$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Costa Rica"] <- max(costa_rica$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Croatia"] <- max(croatia$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Cyprus"] <- max(cyprus$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Czech"] <- max(czech_rep$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Denmark"] <- max(denmark$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Estonia"] <- max(estonia$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Finland"] <- max(finland$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "France"] <- max(france$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Germany"] <- max(germany$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Gibraltar"] <- max(gibraltar$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Greece"] <- max(greece$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Hungary"] <- max(hungary$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Iceland"] <- max(iceland$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "India"] <- max(india$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Indonesia"] <- max(indonesia$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Ireland"] <- max(ireland$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Israel"] <- max(israel$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Italy"] <- max(italy$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Kuwait"] <- max(kuwait$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Latvia"] <- max(latvia$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Lithuania"] <- max(lithuania$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Luxembourg"] <- max(luxembourg$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Malta"] <- max(malta$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Mexico"] <- max(mexico$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Norway"] <- max(norway$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Oman"] <- max(oman$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Panama"] <- max(panama$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Poland"] <- max(poland$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Portugal"] <- max(portugal$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Romania"] <- max(romania$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Russia"] <- max(russia$percentage, na.rm = TRUE)
vaccinations1$percentage[vaccinations$country == "Saudi Arabia"] <- max(saudi_arabia$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Serbia"] <- max(serbia$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Seychelles"] <- max(seychelles$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Slovakia"] <- max(slovakia$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Slovenia"] <- max(slovenia$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Spain"] <- max(spain$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Sweden"] <- max(sweden$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "Turkey"] <- max(turkey$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "UAE"] <- max(uae$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "United Kingdom"] <- max(uk$percentage, na.rm = TRUE) 
vaccinations1$percentage[vaccinations$country == "United States"] <- max(usa$percentage, na.rm = TRUE) 
```

```{r, warning = FALSE}
x =select(vaccinations1, country, percentage)

y = unique(x) %>% drop_na

yincr = y %>% arrange(y$percentage)
yincr = yincr[!grepl("-Inf", yincr$percentage),]


kable1 =head(yincr,7)
kable1 = kbl(kable1)
kable1
```

## Most Vaccinated Country in terms of Population (% of Population)
```{r, warning = FALSE}
ydesc = y %>% arrange(y$percentage ,descending=TRUE)

kable2 =tail(ydesc,7)
kable2 = kbl(kable2)
kable2
```

## Country Population vaccinated at least once (in Map)

#### Install necessary packages
```{r, warning = FALSE, message= FALSE}
library(ggplot2)
install.packages("ggmap")
library(ggmap)
install.packages("maps")
library(maps)
install.packages("mapdata")
library(mapdata)
```

```{r, warning = FALSE}
world <- map_data("world")

world$percentage = NA
world$percentage[world$region == "Argentina"] <- max(argentina$percentage, na.rm = TRUE) 
world$percentage[world$region == "Austria"] <- max(austria$percentage, na.rm = TRUE) 
world$percentage[world$region == "Bahrain"] <- max(bahrain$percentage, na.rm = TRUE) 
world$percentage[world$region == "Belgium"] <- max(belgium$percentage, na.rm = TRUE) 
world$percentage[world$region == "Brazil"] <- max(brazil$percentage, na.rm = TRUE)
world$percentage[world$region == "Bulgaria"] <- max(bulgaria$percentage, na.rm = TRUE) 
world$percentage[world$region == "Canada"] <- max(canada$percentage, na.rm = TRUE) 
world$percentage[world$region == "Chile"] <- max(chile$percentage, na.rm = TRUE)
world$percentage[world$region == "China"] <- max(china$percentage, na.rm = TRUE)
world$percentage[world$region == "Costa Rica"] <- max(costa_rica$percentage, na.rm = TRUE)
world$percentage[world$region == "Croatia"] <- max(croatia$percentage, na.rm = TRUE)
world$percentage[world$region == "Cyprus"] <- max(cyprus$percentage, na.rm = TRUE)
world$percentage[world$region == "Denmark"] <- max(denmark$percentage, na.rm = TRUE)
world$percentage[world$region == "Estonia"] <- max(estonia$percentage, na.rm = TRUE)
world$percentage[world$region == "Finland"] <- max(finland$percentage, na.rm = TRUE)
world$percentage[world$region == "France"] <- max(france$percentage, na.rm = TRUE)
world$percentage[world$region == "Germany"] <- max(germany$percentage, na.rm = TRUE)
world$percentage[world$region == "Gibraltar"] <- max(gibraltar$percentage, na.rm = TRUE)
world$percentage[world$region == "Greece"] <- max(greece$percentage, na.rm = TRUE)
world$percentage[world$region == "Hungary"] <- max(hungary$percentage, na.rm = TRUE)
world$percentage[world$region == "Iceland"] <- max(iceland$percentage, na.rm = TRUE)
world$percentage[world$region == "India"] <- max(india$percentage, na.rm = TRUE)
world$percentage[world$region == "Indonesia"] <- max(indonesia$percentage, na.rm = TRUE)
world$percentage[world$region == "Ireland"] <- max(ireland$percentage, na.rm = TRUE)
world$percentage[world$region == "Israel"] <- max(israel$percentage, na.rm = TRUE)
world$percentage[world$region == "Italy"] <- max(italy$percentage, na.rm = TRUE)
world$percentage[world$region == "Kuwait"] <- max(kuwait$percentage, na.rm = TRUE)
world$percentage[world$region == "Latvia"] <- max(latvia$percentage, na.rm = TRUE)
world$percentage[world$region == "Lithuania"] <- max(lithuania$percentage, na.rm = TRUE)
world$percentage[world$region == "Luxembourg"] <- max(luxembourg$percentage, na.rm = TRUE)
world$percentage[world$region == "Malta"] <- max(malta$percentage, na.rm = TRUE)
world$percentage[world$region == "Mexico"] <- max(mexico$percentage, na.rm = TRUE)
world$percentage[world$region == "Norway"] <- max(norway$percentage, na.rm = TRUE)
world$percentage[world$region == "Oman"] <- max(oman$percentage, na.rm = TRUE)
world$percentage[world$region == "Panama"] <- max(panama$percentage, na.rm = TRUE)
world$percentage[world$region == "Poland"] <- max(poland$percentage, na.rm = TRUE)
world$percentage[world$region == "Portugal"] <- max(portugal$percentage, na.rm = TRUE)
world$percentage[world$region == "Romania"] <- max(romania$percentage, na.rm = TRUE)
world$percentage[world$region == "Russia"] <- max(russia$percentage, na.rm = TRUE)
world$percentage[world$region == "Saudi Arabia"] <- max(saudi_arabia$percentage, na.rm = TRUE)
world$percentage[world$region == "Serbia"] <- max(serbia$percentage, na.rm = TRUE)
world$percentage[world$region == "Seychelles"] <- max(seychelles$percentage, na.rm = TRUE)
world$percentage[world$region == "Slovakia"] <- max(slovakia$percentage, na.rm = TRUE)
world$percentage[world$region == "Slovenia"] <- max(slovenia$percentage, na.rm = TRUE)
world$percentage[world$region == "Spain"] <- max(spain$percentage, na.rm = TRUE)
world$percentage[world$region == "Sweden"] <- max(sweden$percentage, na.rm = TRUE)
world$percentage[world$region == "United Arab Emirates"] <- max(uae$percentage, na.rm = TRUE)
world$percentage[world$region == "USA"] <- max(usa$percentage, na.rm = TRUE)
world$percentage[world$region == "UK"] <- max(uk$percentage, na.rm = TRUE)

ca_base = ggplot(data = world, mapping = aes(x = long, y = lat , group = group)) + 
  coord_fixed(1.3)
```

```{r, warning = FALSE}
elbow_room1 = ca_base + 
      geom_polygon(data = world, aes(fill = percentage), color = "black") +
      geom_polygon(color = "black", fill = NA) +
      theme_bw()

elbow_room1 + scale_fill_gradient(low = "light blue", high = "dark blue", na.value = NA)
```

